From 64a9e4eb89c0bb709ab6b12bc9ac8df26a0d87d8 Mon Sep 17 00:00:00 2001
From: Jeremy Pinto <jerpint@gmail.com>
Date: Fri, 14 Oct 2022 08:50:43 -0400
Subject: [PATCH] Added notebook (#56)

---
 notebooks/bigearthnet_demo.ipynb | 8139 ++++++++++++++++++++++++++++++
 1 file changed, 8139 insertions(+)
 create mode 100644 notebooks/bigearthnet_demo.ipynb

diff --git a/notebooks/bigearthnet_demo.ipynb b/notebooks/bigearthnet_demo.ipynb
new file mode 100644
index 0000000..78e054d
--- /dev/null
+++ b/notebooks/bigearthnet_demo.ipynb
@@ -0,0 +1,8139 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true,
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/jerpint/bigearthnet/blob/add-notebook/notebooks/bigearthnet_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Bigearthnet\n",
+        "\n",
+        "Author: Jeremy Pinto\n",
+        "\n",
+        "Email: jeremy.pinto@mila.quebec\n",
+        "\n",
+        "## Introduction\n",
+        "\n",
+        "This notebook goes through the basic setup of the [bigearthnet repo](https://github.com/jerpint/bigearthnet).\n",
+        "\n",
+        "It will do some basic data analysis and model training.\n",
+        "\n",
+        "This notebook is meant to show a sample setup to facilitate setup, however the code itself is not designed inherently for notebooks and is best run on dedicated machines."
+      ],
+      "metadata": {
+        "id": "uiTOHOGre3NI"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "At the time of writing, colab uses python3.7 whereas our project requires python3.8. The next cell, we simply ignore these requirements. If you are running this notebook locally, ignore the next cell and follow the instructions in the readme to set up your environment."
+      ],
+      "metadata": {
+        "id": "T1A0EUe6L-uj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "### RUNNING THIS CELL IS REQUIRED ON COLAB COLAB ONLY\n",
+        "# Our project requires python 3.8, but colab is at python3.7, \n",
+        "# so we must manually install some dependencies and ignore the project deps...\n",
+        "# If you are running this locally, simply install everything via conda and skip this cell\n",
+        "###\n",
+        "!pip install pytorch_lightning timm hub hydra-core gitpython gdown torch_tb_profiler\n",
+        "!git clone https://github.com/jerpint/bigearthnet\n",
+        "!cd /content/bigearthnet && pip install -e . --no-deps --ignore-requires-python \n",
+        "%cd /content/bigearthnet/bigearthnet\n",
+        "# default $HOME is not where everything gets saved, kind of annoying so let's just overwrite it\n",
+        "%env HOME=/content"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "s1bsR8swW1IA",
+        "outputId": "af996e65-61f8-4ef9-c6b0-b568496082f2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
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+            "Installing collected packages: urllib3, jmespath, ppft, pox, multiprocess, botocore, s3transfer, pathos, smmap, pyjwt, numcodecs, libdeeplake, humbug, boto3, antlr4-python3-runtime, torchmetrics, pyDeprecate, omegaconf, huggingface-hub, gitdb, deeplake, torch-tb-profiler, timm, pytorch-lightning, hydra-core, hub, gitpython\n",
+            "  Attempting uninstall: urllib3\n",
+            "    Found existing installation: urllib3 1.24.3\n",
+            "    Uninstalling urllib3-1.24.3:\n",
+            "      Successfully uninstalled urllib3-1.24.3\n",
+            "Successfully installed antlr4-python3-runtime-4.9.3 boto3-1.24.90 botocore-1.27.90 deeplake-3.0.10 gitdb-4.0.9 gitpython-3.1.29 hub-3.0.0 huggingface-hub-0.10.1 humbug-0.2.7 hydra-core-1.2.0 jmespath-1.0.1 libdeeplake-0.0.20 multiprocess-0.70.13 numcodecs-0.10.2 omegaconf-2.2.3 pathos-0.2.9 pox-0.3.1 ppft-1.7.6.5 pyDeprecate-0.3.2 pyjwt-2.5.0 pytorch-lightning-1.7.7 s3transfer-0.6.0 smmap-5.0.0 timm-0.6.11 torch-tb-profiler-0.4.0 torchmetrics-0.10.0 urllib3-1.25.11\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.colab-display-data+json": {
+              "pip_warning": {
+                "packages": [
+                  "pydevd_plugins"
+                ]
+              }
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cloning into 'bigearthnet'...\n",
+            "remote: Enumerating objects: 772, done.\u001b[K\n",
+            "remote: Counting objects: 100% (229/229), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (140/140), done.\u001b[K\n",
+            "remote: Total 772 (delta 126), reused 105 (delta 84), pack-reused 543\u001b[K\n",
+            "Receiving objects: 100% (772/772), 1.89 MiB | 5.89 MiB/s, done.\n",
+            "Resolving deltas: 100% (407/407), done.\n",
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Obtaining file:///content/bigearthnet\n",
+            "Installing collected packages: bigearthnet\n",
+            "  Running setup.py develop for bigearthnet\n",
+            "Successfully installed bigearthnet\n",
+            "/content/bigearthnet/bigearthnet\n",
+            "env: HOME=/content\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Test your setup\n",
+        "\n",
+        "Here, we simply are running a train loop on the bigearthnet-mini subset. \n",
+        "This should run quickly and display results in under a minute.\n",
+        "The point is to make sure everything is installed correctly and runs end to end."
+      ],
+      "metadata": {
+        "id": "SCQWEmhaMVEm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python train.py"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uyxbMYBlu11D",
+        "outputId": "91938154-06d9-420e-fc5b-04ef5fd32017"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2022-10-13 22:33:56,248][__main__][INFO] - Beginning training...\n",
+            "[2022-10-13 22:33:56,867][bigearthnet.datamodules.bigearthnet_datamodule][INFO] - Downloading bigearthnet-mini dataset to /content/bigearthnet/datasets/bigearthnet-mini.tar\n",
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1X2-NpZ4ExUooAi8tkCBWAlmHZAG_N3Ux\n",
+            "To: /content/bigearthnet/datasets/bigearthnet-mini.tar\n",
+            "100% 9.72M/9.72M [00:00<00:00, 29.9MB/s]\n",
+            "[2022-10-13 22:34:03,143][bigearthnet.datamodules.bigearthnet_datamodule][INFO] - Succesfully downloaded and extracted bigearthnet-mini to /content/bigearthnet/datasets/bigearthnet-mini\n",
+            "/content/bigearthnet/datasets/bigearthnet-mini/train loaded successfully.\n",
+            "/content/bigearthnet/datasets/bigearthnet-mini/val loaded successfully.\n",
+            "/content/bigearthnet/datasets/bigearthnet-mini/test loaded successfully.\n",
+            "GPU available: True (cuda), used: False\n",
+            "TPU available: False, using: 0 TPU cores\n",
+            "IPU available: False, using: 0 IPUs\n",
+            "HPU available: False, using: 0 HPUs\n",
+            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py:1767: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.\n",
+            "  category=PossibleUserWarning,\n",
+            "Missing logger folder: ./lightning_logs\n",
+            "[2022-10-13 22:34:03,871][bigearthnet.datamodules.bigearthnet_datamodule][INFO] - Dataset already present at /content/bigearthnet/datasets/bigearthnet-mini, skipping download.\n",
+            "\n",
+            "  | Name    | Type              | Params\n",
+            "----------------------------------------------\n",
+            "0 | model   | Baseline          | 3.7 M \n",
+            "1 | loss_fn | BCEWithLogitsLoss | 0     \n",
+            "----------------------------------------------\n",
+            "3.7 M     Trainable params\n",
+            "0         Non-trainable params\n",
+            "3.7 M     Total params\n",
+            "14.977    Total estimated model params size (MB)\n",
+            "Sanity Checking: 0it [00:00, ?it/s]/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py:1896: PossibleUserWarning: The number of training batches (5) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
+            "  category=PossibleUserWarning,\n",
+            "[2022-10-13 22:34:05,154][bigearthnet.utils.callbacks][INFO] - Experiment info:\n",
+            "                  config: datamodule:\n",
+            "  _target_: bigearthnet.datamodules.bigearthnet_datamodule.BigEarthNetDataModule\n",
+            "  dataset_dir: ${oc.env:HOME}/bigearthnet/datasets/\n",
+            "  dataset_name: bigearthnet-mini\n",
+            "  batch_size: 16\n",
+            "  num_workers: 0\n",
+            "  transforms: ${transforms.obj}\n",
+            "optimizer:\n",
+            "  name: adam\n",
+            "  lr: 0.0001\n",
+            "logger:\n",
+            "  _target_: pytorch_lightning.loggers.TensorBoardLogger\n",
+            "  save_dir: .\n",
+            "loss:\n",
+            "  class_weights: null\n",
+            "monitor:\n",
+            "  name: f1_score\n",
+            "  mode: max\n",
+            "  patience: 10\n",
+            "callbacks:\n",
+            "- _target_: pytorch_lightning.callbacks.ModelCheckpoint\n",
+            "  save_top_k: 1\n",
+            "  monitor: step\n",
+            "  mode: max\n",
+            "  filename: last-model\n",
+            "- _target_: pytorch_lightning.callbacks.ModelCheckpoint\n",
+            "  save_top_k: 1\n",
+            "  monitor: f1_score/val\n",
+            "  mode: max\n",
+            "  filename: best-model\n",
+            "- _target_: pytorch_lightning.callbacks.EarlyStopping\n",
+            "  monitor: f1_score/val\n",
+            "  mode: max\n",
+            "  patience: 10\n",
+            "- _target_: bigearthnet.utils.callbacks.ReproducibilityLogging\n",
+            "- _target_: bigearthnet.utils.callbacks.MonitorHyperParameters\n",
+            "trainer:\n",
+            "  _target_: pytorch_lightning.Trainer\n",
+            "  callbacks: ${callbacks}\n",
+            "  logger: ${logger}\n",
+            "  max_epochs: 3\n",
+            "  profiler: pytorch\n",
+            "experiment:\n",
+            "  group: ???\n",
+            "  seed: ???\n",
+            "model:\n",
+            "  _target_: bigearthnet.models.nets.baseline.Baseline\n",
+            "  num_classes: 43\n",
+            "  hidden_dim: 256\n",
+            "  model_name: baseline\n",
+            "  pretrained: false\n",
+            "transforms:\n",
+            "  obj:\n",
+            "    _target_: torchvision.transforms.Compose\n",
+            "    transforms:\n",
+            "    - _target_: torchvision.transforms.Normalize\n",
+            "      mean:\n",
+            "      - 439.32135009765625\n",
+            "      - 623.2581176757812\n",
+            "      - 599.888671875\n",
+            "      std:\n",
+            "      - 606.1390380859375\n",
+            "      - 612.572998046875\n",
+            "      - 702.5380249023438\n",
+            "  description:\n",
+            "  - Normalize\n",
+            "\n",
+            "                  hostname: 74c1eff38ccf\n",
+            "                  git code hash: f3f9b107924854c333db2a7049b1f343bc4f8d66\n",
+            "                  git branch name: main\n",
+            "                  dependencies: absl-py==1.2.0\n",
+            "aeppl==0.0.33\n",
+            "aesara==2.7.9\n",
+            "aiohttp==3.8.3\n",
+            "aiosignal==1.2.0\n",
+            "alabaster==0.7.12\n",
+            "albumentations==1.2.1\n",
+            "altair==4.2.0\n",
+            "antlr4-python3-runtime==4.9.3\n",
+            "appdirs==1.4.4\n",
+            "arviz==0.12.1\n",
+            "astor==0.8.1\n",
+            "astropy==4.3.1\n",
+            "astunparse==1.6.3\n",
+            "async-timeout==4.0.2\n",
+            "asynctest==0.13.0\n",
+            "atari-py==0.2.9\n",
+            "atomicwrites==1.4.1\n",
+            "attrs==22.1.0\n",
+            "audioread==3.0.0\n",
+            "autograd==1.5\n",
+            "Babel==2.10.3\n",
+            "backcall==0.2.0\n",
+            "beautifulsoup4==4.6.3\n",
+            "-e git+https://github.com/jerpint/bigearthnet@f3f9b107924854c333db2a7049b1f343bc4f8d66#egg=bigearthnet\n",
+            "bleach==5.0.1\n",
+            "blis==0.7.8\n",
+            "bokeh==2.3.3\n",
+            "boto3==1.24.90\n",
+            "botocore==1.27.90\n",
+            "branca==0.5.0\n",
+            "bs4==0.0.1\n",
+            "CacheControl==0.12.11\n",
+            "cached-property==1.5.2\n",
+            "cachetools==4.2.4\n",
+            "catalogue==2.0.8\n",
+            "certifi==2022.9.24\n",
+            "cffi==1.15.1\n",
+            "cftime==1.6.2\n",
+            "chardet==3.0.4\n",
+            "charset-normalizer==2.1.1\n",
+            "click==7.1.2\n",
+            "clikit==0.6.2\n",
+            "cloudpickle==1.5.0\n",
+            "cmake==3.22.6\n",
+            "cmdstanpy==1.0.7\n",
+            "colorcet==3.0.1\n",
+            "colorlover==0.3.0\n",
+            "community==1.0.0b1\n",
+            "confection==0.0.3\n",
+            "cons==0.4.5\n",
+            "contextlib2==0.5.5\n",
+            "convertdate==2.4.0\n",
+            "crashtest==0.3.1\n",
+            "crcmod==1.7\n",
+            "cufflinks==0.17.3\n",
+            "cupy-cuda11x==11.0.0\n",
+            "cvxopt==1.3.0\n",
+            "cvxpy==1.2.1\n",
+            "cycler==0.11.0\n",
+            "cymem==2.0.6\n",
+            "Cython==0.29.32\n",
+            "daft==0.0.4\n",
+            "dask==2022.2.0\n",
+            "datascience==0.17.5\n",
+            "debugpy==1.0.0\n",
+            "decorator==4.4.2\n",
+            "deeplake==3.0.10\n",
+            "defusedxml==0.7.1\n",
+            "descartes==1.1.0\n",
+            "dill==0.3.5.1\n",
+            "distributed==2022.2.0\n",
+            "dlib==19.24.0\n",
+            "dm-tree==0.1.7\n",
+            "docutils==0.17.1\n",
+            "dopamine-rl==1.0.5\n",
+            "earthengine-api==0.1.326\n",
+            "easydict==1.10\n",
+            "ecos==2.0.10\n",
+            "editdistance==0.5.3\n",
+            "en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.4.0/en_core_web_sm-3.4.0-py3-none-any.whl\n",
+            "entrypoints==0.4\n",
+            "ephem==4.1.3\n",
+            "et-xmlfile==1.1.0\n",
+            "etils==0.8.0\n",
+            "etuples==0.3.8\n",
+            "fa2==0.3.5\n",
+            "fastai==2.7.9\n",
+            "fastcore==1.5.27\n",
+            "fastdownload==0.0.7\n",
+            "fastdtw==0.3.4\n",
+            "fastjsonschema==2.16.2\n",
+            "fastprogress==1.0.3\n",
+            "fastrlock==0.8\n",
+            "feather-format==0.4.1\n",
+            "filelock==3.8.0\n",
+            "firebase-admin==4.4.0\n",
+            "fix-yahoo-finance==0.0.22\n",
+            "Flask==1.1.4\n",
+            "flatbuffers==1.12\n",
+            "folium==0.12.1.post1\n",
+            "frozenlist==1.3.1\n",
+            "fsspec==2022.8.2\n",
+            "future==0.16.0\n",
+            "gast==0.4.0\n",
+            "GDAL==2.2.2\n",
+            "gdown==4.4.0\n",
+            "gensim==3.6.0\n",
+            "geographiclib==1.52\n",
+            "geopy==1.17.0\n",
+            "gin-config==0.5.0\n",
+            "gitdb==4.0.9\n",
+            "GitPython==3.1.29\n",
+            "glob2==0.7\n",
+            "google==2.0.3\n",
+            "google-api-core==1.31.6\n",
+            "google-api-python-client==1.12.11\n",
+            "google-auth==1.35.0\n",
+            "google-auth-httplib2==0.0.4\n",
+            "google-auth-oauthlib==0.4.6\n",
+            "google-cloud-bigquery==1.21.0\n",
+            "google-cloud-bigquery-storage==1.1.2\n",
+            "google-cloud-core==1.0.3\n",
+            "google-cloud-datastore==1.8.0\n",
+            "google-cloud-firestore==1.7.0\n",
+            "google-cloud-language==1.2.0\n",
+            "google-cloud-storage==1.18.1\n",
+            "google-cloud-translate==1.5.0\n",
+            "google-colab @ file:///colabtools/dist/google-colab-1.0.0.tar.gz\n",
+            "google-pasta==0.2.0\n",
+            "google-resumable-media==0.4.1\n",
+            "googleapis-common-protos==1.56.4\n",
+            "googledrivedownloader==0.4\n",
+            "graphviz==0.10.1\n",
+            "greenlet==1.1.3.post0\n",
+            "grpcio==1.49.1\n",
+            "gspread==3.4.2\n",
+            "gspread-dataframe==3.0.8\n",
+            "gym==0.25.2\n",
+            "gym-notices==0.0.8\n",
+            "h5py==3.1.0\n",
+            "HeapDict==1.0.1\n",
+            "hijri-converter==2.2.4\n",
+            "holidays==0.16\n",
+            "holoviews==1.14.9\n",
+            "html5lib==1.0.1\n",
+            "httpimport==0.5.18\n",
+            "httplib2==0.17.4\n",
+            "httplib2shim==0.0.3\n",
+            "httpstan==4.6.1\n",
+            "hub==3.0.0\n",
+            "huggingface-hub==0.10.1\n",
+            "humanize==0.5.1\n",
+            "humbug==0.2.7\n",
+            "hydra-core==1.2.0\n",
+            "hyperopt==0.1.2\n",
+            "idna==2.10\n",
+            "imageio==2.9.0\n",
+            "imagesize==1.4.1\n",
+            "imbalanced-learn==0.8.1\n",
+            "imblearn==0.0\n",
+            "imgaug==0.4.0\n",
+            "importlib-metadata==5.0.0\n",
+            "importlib-resources==5.10.0\n",
+            "imutils==0.5.4\n",
+            "inflect==2.1.0\n",
+            "intel-openmp==2022.2.0\n",
+            "intervaltree==2.1.0\n",
+            "ipykernel==5.3.4\n",
+            "ipython==7.9.0\n",
+            "ipython-genutils==0.2.0\n",
+            "ipython-sql==0.3.9\n",
+            "ipywidgets==7.7.1\n",
+            "itsdangerous==1.1.0\n",
+            "jax==0.3.21\n",
+            "jaxlib @ https://storage.googleapis.com/jax-releases/cuda11/jaxlib-0.3.20+cuda11.cudnn805-cp37-cp37m-manylinux2014_x86_64.whl\n",
+            "jieba==0.42.1\n",
+            "Jinja2==2.11.3\n",
+            "jmespath==1.0.1\n",
+            "joblib==1.2.0\n",
+            "jpeg4py==0.1.4\n",
+            "jsonschema==4.3.3\n",
+            "jupyter-client==6.1.12\n",
+            "jupyter-console==6.1.0\n",
+            "jupyter-core==4.11.1\n",
+            "jupyterlab-widgets==3.0.3\n",
+            "kaggle==1.5.12\n",
+            "kapre==0.3.7\n",
+            "keras==2.9.0\n",
+            "Keras-Preprocessing==1.1.2\n",
+            "keras-vis==0.4.1\n",
+            "kiwisolver==1.4.4\n",
+            "korean-lunar-calendar==0.3.1\n",
+            "langcodes==3.3.0\n",
+            "libclang==14.0.6\n",
+            "libdeeplake==0.0.20\n",
+            "librosa==0.8.1\n",
+            "lightgbm==2.2.3\n",
+            "llvmlite==0.39.1\n",
+            "lmdb==0.99\n",
+            "locket==1.0.0\n",
+            "logical-unification==0.4.5\n",
+            "LunarCalendar==0.0.9\n",
+            "lxml==4.9.1\n",
+            "Markdown==3.4.1\n",
+            "MarkupSafe==2.0.1\n",
+            "marshmallow==3.18.0\n",
+            "matplotlib==3.2.2\n",
+            "matplotlib-venn==0.11.7\n",
+            "miniKanren==1.0.3\n",
+            "missingno==0.5.1\n",
+            "mistune==0.8.4\n",
+            "mizani==0.7.3\n",
+            "mkl==2019.0\n",
+            "mlxtend==0.14.0\n",
+            "more-itertools==8.14.0\n",
+            "moviepy==0.2.3.5\n",
+            "mpmath==1.2.1\n",
+            "msgpack==1.0.4\n",
+            "multidict==6.0.2\n",
+            "multipledispatch==0.6.0\n",
+            "multiprocess==0.70.13\n",
+            "multitasking==0.0.11\n",
+            "murmurhash==1.0.8\n",
+            "music21==5.5.0\n",
+            "natsort==5.5.0\n",
+            "nbconvert==5.6.1\n",
+            "nbformat==5.7.0\n",
+            "netCDF4==1.6.1\n",
+            "networkx==2.6.3\n",
+            "nibabel==3.0.2\n",
+            "nltk==3.7\n",
+            "notebook==5.5.0\n",
+            "numba==0.56.2\n",
+            "numcodecs==0.10.2\n",
+            "numexpr==2.8.3\n",
+            "numpy==1.21.6\n",
+            "oauth2client==4.1.3\n",
+            "oauthlib==3.2.1\n",
+            "okgrade==0.4.3\n",
+            "omegaconf==2.2.3\n",
+            "opencv-contrib-python==4.6.0.66\n",
+            "opencv-python==4.6.0.66\n",
+            "opencv-python-headless==4.6.0.66\n",
+            "openpyxl==3.0.10\n",
+            "opt-einsum==3.3.0\n",
+            "osqp==0.6.2.post0\n",
+            "packaging==21.3\n",
+            "palettable==3.3.0\n",
+            "pandas==1.3.5\n",
+            "pandas-datareader==0.9.0\n",
+            "pandas-gbq==0.13.3\n",
+            "pandas-profiling==1.4.1\n",
+            "pandocfilters==1.5.0\n",
+            "panel==0.12.1\n",
+            "param==1.12.2\n",
+            "parso==0.8.3\n",
+            "partd==1.3.0\n",
+            "pastel==0.2.1\n",
+            "pathlib==1.0.1\n",
+            "pathos==0.2.9\n",
+            "pathy==0.6.2\n",
+            "patsy==0.5.3\n",
+            "pep517==0.13.0\n",
+            "pexpect==4.8.0\n",
+            "pickleshare==0.7.5\n",
+            "Pillow==7.1.2\n",
+            "pip==21.1.3\n",
+            "pip-tools==6.2.0\n",
+            "plotly==5.5.0\n",
+            "plotnine==0.8.0\n",
+            "pluggy==0.7.1\n",
+            "pooch==1.6.0\n",
+            "portpicker==1.3.9\n",
+            "pox==0.3.1\n",
+            "ppft==1.7.6.5\n",
+            "prefetch-generator==1.0.1\n",
+            "preshed==3.0.7\n",
+            "prettytable==3.4.1\n",
+            "progressbar2==3.38.0\n",
+            "promise==2.3\n",
+            "prompt-toolkit==2.0.10\n",
+            "prophet==1.1.1\n",
+            "protobuf==3.17.3\n",
+            "psutil==5.4.8\n",
+            "psycopg2==2.9.4\n",
+            "ptyprocess==0.7.0\n",
+            "py==1.11.0\n",
+            "pyarrow==6.0.1\n",
+            "pyasn1==0.4.8\n",
+            "pyasn1-modules==0.2.8\n",
+            "pycocotools==2.0.5\n",
+            "pycparser==2.21\n",
+            "pyct==0.4.8\n",
+            "pydantic==1.9.2\n",
+            "pydata-google-auth==1.4.0\n",
+            "pyDeprecate==0.3.2\n",
+            "pydot==1.3.0\n",
+            "pydot-ng==2.0.0\n",
+            "pydotplus==2.0.2\n",
+            "PyDrive==1.3.1\n",
+            "pyemd==0.5.1\n",
+            "pyerfa==2.0.0.1\n",
+            "Pygments==2.6.1\n",
+            "pygobject==3.26.1\n",
+            "PyJWT==2.5.0\n",
+            "pylev==1.4.0\n",
+            "pymc==4.1.4\n",
+            "PyMeeus==0.5.11\n",
+            "pymongo==4.2.0\n",
+            "pymystem3==0.2.0\n",
+            "PyOpenGL==3.1.6\n",
+            "pyparsing==3.0.9\n",
+            "pyrsistent==0.18.1\n",
+            "pysimdjson==3.2.0\n",
+            "pysndfile==1.3.8\n",
+            "PySocks==1.7.1\n",
+            "pystan==3.3.0\n",
+            "pytest==3.6.4\n",
+            "python-apt==0.0.0\n",
+            "python-chess==0.23.11\n",
+            "python-dateutil==2.8.2\n",
+            "python-louvain==0.16\n",
+            "python-slugify==6.1.2\n",
+            "python-utils==3.3.3\n",
+            "pytorch-lightning==1.7.7\n",
+            "pytz==2022.4\n",
+            "pyviz-comms==2.2.1\n",
+            "PyWavelets==1.3.0\n",
+            "PyYAML==6.0\n",
+            "pyzmq==23.2.1\n",
+            "qdldl==0.1.5.post2\n",
+            "qudida==0.0.4\n",
+            "regex==2022.6.2\n",
+            "requests==2.23.0\n",
+            "requests-oauthlib==1.3.1\n",
+            "resampy==0.4.2\n",
+            "rpy2==3.4.5\n",
+            "rsa==4.9\n",
+            "s3transfer==0.6.0\n",
+            "scikit-image==0.18.3\n",
+            "scikit-learn==1.0.2\n",
+            "scipy==1.7.3\n",
+            "screen-resolution-extra==0.0.0\n",
+            "scs==3.2.0\n",
+            "seaborn==0.11.2\n",
+            "Send2Trash==1.8.0\n",
+            "setuptools==57.4.0\n",
+            "setuptools-git==1.2\n",
+            "Shapely==1.8.4\n",
+            "six==1.15.0\n",
+            "sklearn-pandas==1.8.0\n",
+            "smart-open==5.2.1\n",
+            "smmap==5.0.0\n",
+            "snowballstemmer==2.2.0\n",
+            "sortedcontainers==2.4.0\n",
+            "soundfile==0.11.0\n",
+            "spacy==3.4.1\n",
+            "spacy-legacy==3.0.10\n",
+            "spacy-loggers==1.0.3\n",
+            "Sphinx==1.8.6\n",
+            "sphinxcontrib-serializinghtml==1.1.5\n",
+            "sphinxcontrib-websupport==1.2.4\n",
+            "SQLAlchemy==1.4.41\n",
+            "sqlparse==0.4.3\n",
+            "srsly==2.4.4\n",
+            "statsmodels==0.12.2\n",
+            "sympy==1.7.1\n",
+            "tables==3.7.0\n",
+            "tabulate==0.8.10\n",
+            "tblib==1.7.0\n",
+            "tenacity==8.1.0\n",
+            "tensorboard==2.9.1\n",
+            "tensorboard-data-server==0.6.1\n",
+            "tensorboard-plugin-wit==1.8.1\n",
+            "tensorflow==2.9.2\n",
+            "tensorflow-datasets==4.6.0\n",
+            "tensorflow-estimator==2.9.0\n",
+            "tensorflow-gcs-config==2.9.1\n",
+            "tensorflow-hub==0.12.0\n",
+            "tensorflow-io-gcs-filesystem==0.27.0\n",
+            "tensorflow-metadata==1.10.0\n",
+            "tensorflow-probability==0.16.0\n",
+            "termcolor==2.0.1\n",
+            "terminado==0.13.3\n",
+            "testpath==0.6.0\n",
+            "text-unidecode==1.3\n",
+            "textblob==0.15.3\n",
+            "thinc==8.1.3\n",
+            "threadpoolctl==3.1.0\n",
+            "tifffile==2021.11.2\n",
+            "timm==0.6.11\n",
+            "toml==0.10.2\n",
+            "tomli==2.0.1\n",
+            "toolz==0.12.0\n",
+            "torch @ https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp37-cp37m-linux_x86_64.whl\n",
+            "torch-tb-profiler==0.4.0\n",
+            "torchaudio @ https://download.pytorch.org/whl/cu113/torchaudio-0.12.1%2Bcu113-cp37-cp37m-linux_x86_64.whl\n",
+            "torchmetrics==0.10.0\n",
+            "torchsummary==1.5.1\n",
+            "torchtext==0.13.1\n",
+            "torchvision @ https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp37-cp37m-linux_x86_64.whl\n",
+            "tornado==5.1.1\n",
+            "tqdm==4.64.1\n",
+            "traitlets==5.1.1\n",
+            "tweepy==3.10.0\n",
+            "typeguard==2.7.1\n",
+            "typer==0.4.2\n",
+            "typing-extensions==4.1.1\n",
+            "tzlocal==1.5.1\n",
+            "ujson==5.5.0\n",
+            "uritemplate==3.0.1\n",
+            "urllib3==1.25.11\n",
+            "vega-datasets==0.9.0\n",
+            "wasabi==0.10.1\n",
+            "wcwidth==0.2.5\n",
+            "webargs==8.2.0\n",
+            "webencodings==0.5.1\n",
+            "Werkzeug==1.0.1\n",
+            "wheel==0.37.1\n",
+            "widgetsnbextension==3.6.1\n",
+            "wordcloud==1.8.2.2\n",
+            "wrapt==1.14.1\n",
+            "xarray==0.20.2\n",
+            "xarray-einstats==0.2.2\n",
+            "xgboost==0.90\n",
+            "xkit==0.0.0\n",
+            "xlrd==1.1.0\n",
+            "xlwt==1.3.0\n",
+            "yarl==1.8.1\n",
+            "yellowbrick==1.5\n",
+            "zict==2.2.0\n",
+            "zipp==3.9.0\n",
+            "                  \n",
+            "\n",
+            "[2022-10-13 22:34:07,610][numexpr.utils][INFO] - NumExpr defaulting to 2 threads.\n",
+            "Epoch 0:   0% 0/8 [00:00<?, ?it/s] /content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "\n",
+            "Validation: 0it [00:00, ?it/s]\u001b[A/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "\n",
+            "Validation:   0% 0/3 [00:00<?, ?it/s]\u001b[A\n",
+            "Validation DataLoader 0:   0% 0/3 [00:00<?, ?it/s]\u001b[A\n",
+            "Epoch 0: 100% 8/8 [00:04<00:00,  1.99it/s][2022-10-13 22:34:11,905][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    val Epoch: 0\n",
+            "    val Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.00      0.00      0.00         1\n",
+            "                                                                              Airports       0.00      0.00      0.00         0\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00         0\n",
+            "                                                                             Bare rock       0.00      0.00      0.00         0\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00         0\n",
+            "                                                                   Broad-leaved forest       0.00      0.00      0.00         7\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00         0\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00         0\n",
+            "                                                          Complex cultivation patterns       0.00      0.00      0.00         6\n",
+            "                                                                     Coniferous forest       0.00      0.00      0.00        15\n",
+            "                                                                    Construction sites       0.00      0.00      0.00         0\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00         5\n",
+            "                                                            Discontinuous urban fabric       0.15      1.00      0.26         6\n",
+            "                                                                            Dump sites       0.00      0.00      0.00         0\n",
+            "                                                                             Estuaries       0.00      0.00      0.00         0\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00         1\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00         0\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00         0\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00         0\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00         0\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.17      1.00      0.30         7\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00         0\n",
+            "                                                                          Mixed forest       0.40      1.00      0.57        16\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00         0\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00         0\n",
+            "                                                             Non-irrigated arable land       0.30      1.00      0.46        12\n",
+            "                                                                          Olive groves       0.00      0.00      0.00         0\n",
+            "                                                                              Pastures       0.00      0.00      0.00         3\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00         2\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00         0\n",
+            "                                                                            Port areas       0.00      0.00      0.00         0\n",
+            "                                                                           Rice fields       0.00      0.00      0.00         0\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00         0\n",
+            "                                                                               Salines       0.00      0.00      0.00         0\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00         0\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00         1\n",
+            "                                                                         Sea and ocean       0.00      0.00      0.00         5\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00         0\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00         1\n",
+            "                                                           Transitional woodland/shrub       0.29      0.36      0.32        11\n",
+            "                                                                             Vineyards       0.00      0.00      0.00         2\n",
+            "                                                                          Water bodies       0.00      0.00      0.00         3\n",
+            "                                                                         Water courses       0.00      0.00      0.00         0\n",
+            "\n",
+            "                                                                             micro avg       0.18      0.43      0.25       104\n",
+            "                                                                             macro avg       0.03      0.10      0.04       104\n",
+            "                                                                          weighted avg       0.15      0.43      0.21       104\n",
+            "                                                                           samples avg       0.18      0.36      0.23       104\n",
+            "\n",
+            "    val Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[39  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Airports\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[33  0]\n",
+            " [ 7  0]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[34  0]\n",
+            " [ 6  0]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[25  0]\n",
+            " [15  0]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[35  0]\n",
+            " [ 5  0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[ 0 34]\n",
+            " [ 0  6]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[39  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[ 0 40]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[ 0 33]\n",
+            " [ 0  7]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[ 3 37]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[ 0 24]\n",
+            " [ 0 16]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[ 0 28]\n",
+            " [ 0 12]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[37  0]\n",
+            " [ 3  0]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[38  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Salines\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[39  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[35  0]\n",
+            " [ 5  0]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[39  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[19 10]\n",
+            " [ 7  4]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[38  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[37  0]\n",
+            " [ 3  0]]\n",
+            "\n",
+            "Water courses\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 0: 100% 8/8 [00:06<00:00,  1.28it/s, loss=0.663, v_num=0, loss/train_step=0.634, loss/val_step=0.594, loss/val_epoch=0.601]\n",
+            "                                                          \u001b[A[2022-10-13 22:34:14,090][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    train Epoch: 0\n",
+            "    train Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.00      0.00      0.00         7\n",
+            "                                                                              Airports       0.00      0.00      0.00         0\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00         1\n",
+            "                                                                             Bare rock       0.00      0.00      0.00         1\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00         0\n",
+            "                                                                   Broad-leaved forest       0.30      0.25      0.27        28\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00         0\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00         1\n",
+            "                                                          Complex cultivation patterns       0.17      0.07      0.10        14\n",
+            "                                                                     Coniferous forest       0.00      0.00      0.00        30\n",
+            "                                                                    Construction sites       0.00      0.00      0.00         0\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00         0\n",
+            "                                                            Discontinuous urban fabric       0.11      1.00      0.20         9\n",
+            "                                                                            Dump sites       0.00      0.00      0.00         0\n",
+            "                                                                             Estuaries       0.00      0.00      0.00         0\n",
+            "                                                     Fruit trees and berry plantations       0.06      1.00      0.12         2\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00         0\n",
+            "                                                        Industrial or commercial units       0.02      0.50      0.04         2\n",
+            "                                                                        Inland marshes       0.04      0.33      0.07         3\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00         0\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.21      0.76      0.33        17\n",
+            "                                                              Mineral extraction sites       0.01      1.00      0.02         1\n",
+            "                                                                          Mixed forest       0.34      1.00      0.50        27\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00         0\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00         2\n",
+            "                                                             Non-irrigated arable land       0.34      0.85      0.48        26\n",
+            "                                                                          Olive groves       0.00      0.00      0.00         2\n",
+            "                                                                              Pastures       0.25      0.57      0.35        14\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00         1\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00         2\n",
+            "                                                                            Port areas       0.00      0.00      0.00         0\n",
+            "                                                                           Rice fields       0.00      0.00      0.00         1\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00         0\n",
+            "                                                                               Salines       0.00      0.00      0.00         0\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00         0\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00         1\n",
+            "                                                                         Sea and ocean       0.11      0.20      0.14        10\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00         0\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00         2\n",
+            "                                                           Transitional woodland/shrub       0.44      0.48      0.46        29\n",
+            "                                                                             Vineyards       0.06      1.00      0.12         2\n",
+            "                                                                          Water bodies       0.16      0.33      0.22        12\n",
+            "                                                                         Water courses       0.00      0.00      0.00         0\n",
+            "\n",
+            "                                                                             micro avg       0.12      0.46      0.19       247\n",
+            "                                                                             macro avg       0.06      0.22      0.08       247\n",
+            "                                                                          weighted avg       0.21      0.46      0.27       247\n",
+            "                                                                           samples avg       0.13      0.44      0.19       247\n",
+            "\n",
+            "    train Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[60 13]\n",
+            " [ 7  0]]\n",
+            "\n",
+            "Airports\n",
+            "[[79  1]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[79  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[79  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[36 16]\n",
+            " [21  7]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[78  2]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[78  1]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[61  5]\n",
+            " [13  1]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[50  0]\n",
+            " [30  0]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[ 0 71]\n",
+            " [ 0  9]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[49 31]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[45 35]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[48 30]\n",
+            " [ 0  2]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[78  2]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[25 53]\n",
+            " [ 1  1]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[51 26]\n",
+            " [ 2  1]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[42 38]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[15 48]\n",
+            " [ 4 13]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[ 1 78]\n",
+            " [ 0  1]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[ 0 53]\n",
+            " [ 0 27]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[78  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[11 43]\n",
+            " [ 4 22]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[78  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[42 24]\n",
+            " [ 6  8]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[64 15]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[78  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[24 55]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Salines\n",
+            "[[48 32]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[46 34]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[60 19]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[53 17]\n",
+            " [ 8  2]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[61 19]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[78  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[33 18]\n",
+            " [15 14]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[48 30]\n",
+            " [ 0  2]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[47 21]\n",
+            " [ 8  4]]\n",
+            "\n",
+            "Water courses\n",
+            "[[79  1]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 1:   0% 0/8 [00:00<?, ?it/s, loss=0.663, v_num=0, loss/train_step=0.634, loss/val_step=0.594, loss/val_epoch=0.601, loss/train_epoch=0.663]/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "\n",
+            "Validation: 0it [00:00, ?it/s]\u001b[A/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "\n",
+            "Validation:   0% 0/3 [00:00<?, ?it/s]\u001b[A\n",
+            "Validation DataLoader 0:   0% 0/3 [00:00<?, ?it/s]\u001b[A\n",
+            "Epoch 1: 100% 8/8 [00:02<00:00,  3.16it/s, loss=0.663, v_num=0, loss/train_step=0.634, loss/val_step=0.594, loss/val_epoch=0.601, loss/train_epoch=0.663][2022-10-13 22:34:16,952][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    val Epoch: 1\n",
+            "    val Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.00      0.00      0.00         1\n",
+            "                                                                              Airports       0.00      0.00      0.00         0\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00         0\n",
+            "                                                                             Bare rock       0.00      0.00      0.00         0\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00         0\n",
+            "                                                                   Broad-leaved forest       0.00      0.00      0.00         7\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00         0\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00         0\n",
+            "                                                          Complex cultivation patterns       0.00      0.00      0.00         6\n",
+            "                                                                     Coniferous forest       0.00      0.00      0.00        15\n",
+            "                                                                    Construction sites       0.00      0.00      0.00         0\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00         5\n",
+            "                                                            Discontinuous urban fabric       0.00      0.00      0.00         6\n",
+            "                                                                            Dump sites       0.00      0.00      0.00         0\n",
+            "                                                                             Estuaries       0.00      0.00      0.00         0\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00         1\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00         0\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00         0\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00         0\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00         0\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.17      1.00      0.30         7\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00         0\n",
+            "                                                                          Mixed forest       0.40      1.00      0.57        16\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00         0\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00         0\n",
+            "                                                             Non-irrigated arable land       0.33      0.92      0.49        12\n",
+            "                                                                          Olive groves       0.00      0.00      0.00         0\n",
+            "                                                                              Pastures       0.00      0.00      0.00         3\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00         2\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00         0\n",
+            "                                                                            Port areas       0.00      0.00      0.00         0\n",
+            "                                                                           Rice fields       0.00      0.00      0.00         0\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00         0\n",
+            "                                                                               Salines       0.00      0.00      0.00         0\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00         0\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00         1\n",
+            "                                                                         Sea and ocean       0.00      0.00      0.00         5\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00         0\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00         1\n",
+            "                                                           Transitional woodland/shrub       0.00      0.00      0.00        11\n",
+            "                                                                             Vineyards       0.00      0.00      0.00         2\n",
+            "                                                                          Water bodies       0.00      0.00      0.00         3\n",
+            "                                                                         Water courses       0.00      0.00      0.00         0\n",
+            "\n",
+            "                                                                             micro avg       0.22      0.33      0.26       104\n",
+            "                                                                             macro avg       0.02      0.07      0.03       104\n",
+            "                                                                          weighted avg       0.11      0.33      0.16       104\n",
+            "                                                                           samples avg       0.21      0.27      0.23       104\n",
+            "\n",
+            "    val Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[39  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Airports\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[33  0]\n",
+            " [ 7  0]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[34  0]\n",
+            " [ 6  0]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[25  0]\n",
+            " [15  0]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[35  0]\n",
+            " [ 5  0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[31  3]\n",
+            " [ 6  0]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[39  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[ 0 40]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[ 0 33]\n",
+            " [ 0  7]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[38  2]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[ 0 24]\n",
+            " [ 0 16]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[ 6 22]\n",
+            " [ 1 11]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[37  0]\n",
+            " [ 3  0]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[38  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Salines\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[39  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[35  0]\n",
+            " [ 5  0]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[39  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[29  0]\n",
+            " [11  0]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[38  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[37  0]\n",
+            " [ 3  0]]\n",
+            "\n",
+            "Water courses\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 1: 100% 8/8 [00:04<00:00,  1.79it/s, loss=0.609, v_num=0, loss/train_step=0.507, loss/val_step=0.437, loss/val_epoch=0.457, loss/train_epoch=0.663]\n",
+            "                                                          \u001b[A[2022-10-13 22:34:18,942][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    train Epoch: 1\n",
+            "    train Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.00      0.00      0.00         7\n",
+            "                                                                              Airports       0.00      0.00      0.00         0\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00         1\n",
+            "                                                                             Bare rock       0.00      0.00      0.00         1\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00         0\n",
+            "                                                                   Broad-leaved forest       0.00      0.00      0.00        28\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00         0\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00         1\n",
+            "                                                          Complex cultivation patterns       0.00      0.00      0.00        14\n",
+            "                                                                     Coniferous forest       0.00      0.00      0.00        30\n",
+            "                                                                    Construction sites       0.00      0.00      0.00         0\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00         0\n",
+            "                                                            Discontinuous urban fabric       0.17      0.89      0.28         9\n",
+            "                                                                            Dump sites       0.00      0.00      0.00         0\n",
+            "                                                                             Estuaries       0.00      0.00      0.00         0\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00         2\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00         0\n",
+            "                                                        Industrial or commercial units       0.03      1.00      0.05         2\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00         3\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00         0\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.21      1.00      0.35        17\n",
+            "                                                              Mineral extraction sites       0.01      1.00      0.03         1\n",
+            "                                                                          Mixed forest       0.34      1.00      0.50        27\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00         0\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00         2\n",
+            "                                                             Non-irrigated arable land       0.33      1.00      0.49        26\n",
+            "                                                                          Olive groves       0.00      0.00      0.00         2\n",
+            "                                                                              Pastures       0.00      0.00      0.00        14\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00         1\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00         2\n",
+            "                                                                            Port areas       0.00      0.00      0.00         0\n",
+            "                                                                           Rice fields       0.00      0.00      0.00         1\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00         0\n",
+            "                                                                               Salines       0.00      0.00      0.00         0\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00         0\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00         1\n",
+            "                                                                         Sea and ocean       0.00      0.00      0.00        10\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00         0\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00         2\n",
+            "                                                           Transitional woodland/shrub       0.22      0.07      0.11        29\n",
+            "                                                                             Vineyards       0.00      0.00      0.00         2\n",
+            "                                                                          Water bodies       0.00      0.00      0.00        12\n",
+            "                                                                         Water courses       0.00      0.00      0.00         0\n",
+            "\n",
+            "                                                                             micro avg       0.19      0.34      0.24       247\n",
+            "                                                                             macro avg       0.03      0.14      0.04       247\n",
+            "                                                                          weighted avg       0.12      0.34      0.15       247\n",
+            "                                                                           samples avg       0.19      0.31      0.22       247\n",
+            "\n",
+            "    train Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[73  0]\n",
+            " [ 7  0]]\n",
+            "\n",
+            "Airports\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[79  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[79  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[52  0]\n",
+            " [28  0]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[79  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[66  0]\n",
+            " [14  0]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[50  0]\n",
+            " [30  0]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[31 40]\n",
+            " [ 1  8]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[78  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[ 0 78]\n",
+            " [ 0  2]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[77  0]\n",
+            " [ 3  0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[ 0 63]\n",
+            " [ 0 17]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[12 67]\n",
+            " [ 0  1]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[ 0 53]\n",
+            " [ 0 27]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[78  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[ 0 54]\n",
+            " [ 0 26]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[78  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[66  0]\n",
+            " [14  0]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[79  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[78  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[79  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Salines\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[79  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[70  0]\n",
+            " [10  0]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[78  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[44  7]\n",
+            " [27  2]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[78  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[68  0]\n",
+            " [12  0]]\n",
+            "\n",
+            "Water courses\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 2:   0% 0/8 [00:00<?, ?it/s, loss=0.609, v_num=0, loss/train_step=0.507, loss/val_step=0.437, loss/val_epoch=0.457, loss/train_epoch=0.555]/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "\n",
+            "Validation: 0it [00:00, ?it/s]\u001b[A/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "\n",
+            "Validation:   0% 0/3 [00:00<?, ?it/s]\u001b[A\n",
+            "Validation DataLoader 0:   0% 0/3 [00:00<?, ?it/s]\u001b[A\n",
+            "Epoch 2: 100% 8/8 [00:03<00:00,  2.53it/s, loss=0.609, v_num=0, loss/train_step=0.507, loss/val_step=0.437, loss/val_epoch=0.457, loss/train_epoch=0.555][2022-10-13 22:34:22,617][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    val Epoch: 2\n",
+            "    val Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.00      0.00      0.00         1\n",
+            "                                                                              Airports       0.00      0.00      0.00         0\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00         0\n",
+            "                                                                             Bare rock       0.00      0.00      0.00         0\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00         0\n",
+            "                                                                   Broad-leaved forest       0.00      0.00      0.00         7\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00         0\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00         0\n",
+            "                                                          Complex cultivation patterns       0.00      0.00      0.00         6\n",
+            "                                                                     Coniferous forest       0.00      0.00      0.00        15\n",
+            "                                                                    Construction sites       0.00      0.00      0.00         0\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00         5\n",
+            "                                                            Discontinuous urban fabric       0.00      0.00      0.00         6\n",
+            "                                                                            Dump sites       0.00      0.00      0.00         0\n",
+            "                                                                             Estuaries       0.00      0.00      0.00         0\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00         1\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00         0\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00         0\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00         0\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00         0\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.17      1.00      0.30         7\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00         0\n",
+            "                                                                          Mixed forest       0.40      1.00      0.57        16\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00         0\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00         0\n",
+            "                                                             Non-irrigated arable land       0.00      0.00      0.00        12\n",
+            "                                                                          Olive groves       0.00      0.00      0.00         0\n",
+            "                                                                              Pastures       0.00      0.00      0.00         3\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00         2\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00         0\n",
+            "                                                                            Port areas       0.00      0.00      0.00         0\n",
+            "                                                                           Rice fields       0.00      0.00      0.00         0\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00         0\n",
+            "                                                                               Salines       0.00      0.00      0.00         0\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00         0\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00         1\n",
+            "                                                                         Sea and ocean       0.00      0.00      0.00         5\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00         0\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00         1\n",
+            "                                                           Transitional woodland/shrub       0.00      0.00      0.00        11\n",
+            "                                                                             Vineyards       0.00      0.00      0.00         2\n",
+            "                                                                          Water bodies       0.00      0.00      0.00         3\n",
+            "                                                                         Water courses       0.00      0.00      0.00         0\n",
+            "\n",
+            "                                                                             micro avg       0.29      0.22      0.25       104\n",
+            "                                                                             macro avg       0.01      0.05      0.02       104\n",
+            "                                                                          weighted avg       0.07      0.22      0.11       104\n",
+            "                                                                           samples avg       0.29      0.16      0.21       104\n",
+            "\n",
+            "    val Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[39  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Airports\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[33  0]\n",
+            " [ 7  0]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[34  0]\n",
+            " [ 6  0]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[25  0]\n",
+            " [15  0]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[35  0]\n",
+            " [ 5  0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[34  0]\n",
+            " [ 6  0]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[39  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[ 0 33]\n",
+            " [ 0  7]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[ 0 24]\n",
+            " [ 0 16]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[28  0]\n",
+            " [12  0]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[37  0]\n",
+            " [ 3  0]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[38  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Salines\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[39  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[35  0]\n",
+            " [ 5  0]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[39  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[29  0]\n",
+            " [11  0]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[38  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[37  0]\n",
+            " [ 3  0]]\n",
+            "\n",
+            "Water courses\n",
+            "[[40  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 2: 100% 8/8 [00:03<00:00,  2.52it/s, loss=0.542, v_num=0, loss/train_step=0.345, loss/val_step=0.295, loss/val_epoch=0.314, loss/train_epoch=0.555]\n",
+            "                                                          \u001b[A[2022-10-13 22:34:22,634][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    train Epoch: 2\n",
+            "    train Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.00      0.00      0.00         7\n",
+            "                                                                              Airports       0.00      0.00      0.00         0\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00         1\n",
+            "                                                                             Bare rock       0.00      0.00      0.00         1\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00         0\n",
+            "                                                                   Broad-leaved forest       0.00      0.00      0.00        28\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00         0\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00         1\n",
+            "                                                          Complex cultivation patterns       0.00      0.00      0.00        14\n",
+            "                                                                     Coniferous forest       0.00      0.00      0.00        30\n",
+            "                                                                    Construction sites       0.00      0.00      0.00         0\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00         0\n",
+            "                                                            Discontinuous urban fabric       0.00      0.00      0.00         9\n",
+            "                                                                            Dump sites       0.00      0.00      0.00         0\n",
+            "                                                                             Estuaries       0.00      0.00      0.00         0\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00         2\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00         0\n",
+            "                                                        Industrial or commercial units       0.03      1.00      0.06         2\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00         3\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00         0\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.21      1.00      0.35        17\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00         1\n",
+            "                                                                          Mixed forest       0.34      1.00      0.50        27\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00         0\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00         2\n",
+            "                                                             Non-irrigated arable land       0.52      0.54      0.53        26\n",
+            "                                                                          Olive groves       0.00      0.00      0.00         2\n",
+            "                                                                              Pastures       0.00      0.00      0.00        14\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00         1\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00         2\n",
+            "                                                                            Port areas       0.00      0.00      0.00         0\n",
+            "                                                                           Rice fields       0.00      0.00      0.00         1\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00         0\n",
+            "                                                                               Salines       0.00      0.00      0.00         0\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00         0\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00         1\n",
+            "                                                                         Sea and ocean       0.00      0.00      0.00        10\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00         0\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00         2\n",
+            "                                                           Transitional woodland/shrub       0.00      0.00      0.00        29\n",
+            "                                                                             Vineyards       0.00      0.00      0.00         2\n",
+            "                                                                          Water bodies       0.00      0.00      0.00        12\n",
+            "                                                                         Water courses       0.00      0.00      0.00         0\n",
+            "\n",
+            "                                                                             micro avg       0.24      0.24      0.24       247\n",
+            "                                                                             macro avg       0.03      0.08      0.03       247\n",
+            "                                                                          weighted avg       0.11      0.24      0.14       247\n",
+            "                                                                           samples avg       0.24      0.23      0.22       247\n",
+            "\n",
+            "    train Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[73  0]\n",
+            " [ 7  0]]\n",
+            "\n",
+            "Airports\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[79  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[79  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[52  0]\n",
+            " [28  0]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[79  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[66  0]\n",
+            " [14  0]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[50  0]\n",
+            " [30  0]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[71  0]\n",
+            " [ 9  0]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[78  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[17 61]\n",
+            " [ 0  2]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[77  0]\n",
+            " [ 3  0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[ 0 63]\n",
+            " [ 0 17]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[79  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[ 0 53]\n",
+            " [ 0 27]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[78  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[41 13]\n",
+            " [12 14]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[78  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[66  0]\n",
+            " [14  0]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[79  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[78  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[79  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Salines\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[79  0]\n",
+            " [ 1  0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[70  0]\n",
+            " [10  0]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[78  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[51  0]\n",
+            " [29  0]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[78  0]\n",
+            " [ 2  0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[68  0]\n",
+            " [12  0]]\n",
+            "\n",
+            "Water courses\n",
+            "[[80  0]\n",
+            " [ 0  0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 2: 100% 8/8 [00:03<00:00,  2.45it/s, loss=0.542, v_num=0, loss/train_step=0.345, loss/val_step=0.295, loss/val_epoch=0.314, loss/train_epoch=0.408]`Trainer.fit` stopped: `max_epochs=3` reached.\n",
+            "Epoch 2: 100% 8/8 [00:03<00:00,  2.34it/s, loss=0.542, v_num=0, loss/train_step=0.345, loss/val_step=0.295, loss/val_epoch=0.314, loss/train_epoch=0.408]\n",
+            "FIT Profiler Report\n",
+            "Profile stats for: records\n",
+            "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
+            "                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg    # of Calls  \n",
+            "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
+            "                                          ProfilerStep*         7.46%      19.814ms        99.99%     265.526ms     132.763ms             2  \n",
+            "[pl][profile][Strategy]SingleDeviceStrategy.validati...         0.40%       1.055ms        72.36%     192.169ms      96.085ms             2  \n",
+            "[pl][module]bigearthnet.models.nets.baseline.Baselin...         0.08%     201.000us        71.75%     190.537ms      95.269ms             2  \n",
+            "[pl][module]torch.nn.modules.container.Sequential: m...         0.84%       2.243ms        69.06%     183.403ms      91.701ms             2  \n",
+            "                                           aten::conv2d         0.01%      23.000us        37.23%      98.879ms      16.480ms             6  \n",
+            "                                aten::_convolution_mode         0.01%      33.000us        37.23%      98.856ms      16.476ms             6  \n",
+            "                                     aten::_convolution         0.03%      89.000us        37.21%      98.823ms      16.471ms             6  \n",
+            "                               aten::mkldnn_convolution        37.16%      98.691ms        37.18%      98.734ms      16.456ms             6  \n",
+            "                                       aten::max_pool2d         0.20%     533.000us        26.19%      69.547ms      11.591ms             6  \n",
+            "                          aten::max_pool2d_with_indices        25.99%      69.014ms        25.99%      69.014ms      11.502ms             6  \n",
+            "[pl][module]torch.nn.modules.conv.Conv2d: model.conv...         0.05%     127.000us        21.25%      56.429ms      28.215ms             2  \n",
+            "[pl][profile][EvaluationEpochLoop].val_dataloader_id...         0.19%     517.000us        19.73%      52.404ms      26.202ms             2  \n",
+            "enumerate(DataLoader)#_SingleProcessDataLoaderIter._...        15.03%      39.910ms        19.54%      51.879ms      25.939ms             2  \n",
+            "[pl][module]torch.nn.modules.pooling.MaxPool2d: mode...         0.05%     136.000us        18.93%      50.280ms      25.140ms             2  \n",
+            "[pl][module]torch.nn.modules.conv.Conv2d: model.conv...         0.04%     113.000us        11.57%      30.723ms      15.361ms             2  \n",
+            "[pl][module]torch.nn.modules.pooling.MaxPool2d: mode...         0.04%     108.000us         4.90%      13.008ms       6.504ms             2  \n",
+            "[pl][module]torch.nn.modules.conv.Conv2d: model.conv...         0.04%     113.000us         4.55%      12.084ms       6.042ms             2  \n",
+            "                                             aten::relu         0.02%      54.000us         4.38%      11.635ms       1.454ms             8  \n",
+            "                                        aten::clamp_min         4.36%      11.581ms         4.36%      11.581ms       1.448ms             8  \n",
+            "[pl][module]torch.nn.modules.activation.ReLU: model....         0.04%     101.000us         3.26%       8.661ms       4.330ms             2  \n",
+            "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
+            "Self CPU time total: 265.560ms\n",
+            "\n",
+            "[2022-10-13 22:34:22,968][__main__][INFO] - Training Done.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Dataset exploration\n",
+        "\n",
+        "Let's take a look at some of the data we are working with. Note that we are exploring bigearthnet-medium here. This dataset is approximately 3gb to download. Bigearthnet-full will be about 30gb so will take much longer to gather statistics and download. "
+      ],
+      "metadata": {
+        "id": "3UkbrTJfvAJo"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# code to load our datasets into memory\n",
+        "\n",
+        "import pathlib\n",
+        "import numpy as np\n",
+        "import yaml\n",
+        "\n",
+        "from bigearthnet.datamodules.bigearthnet_datamodule import BigEarthNetDataModule\n",
+        "\n",
+        "dataset_dir = \"../datasets/\" # root directory where to download the datasets\n",
+        "dataset_name = \"bigearthnet-medium\" # One of bigearthnet-mini, bigearthnet-medium, bigearthnet-full\n",
+        "batch_size = 16\n",
+        "\n",
+        "dm = BigEarthNetDataModule(dataset_dir, dataset_name, batch_size=batch_size)\n",
+        "dm.setup()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mUwkVHIHnkle",
+        "outputId": "d30cc5e9-eabb-426c-d6ed-f33c71a16f05"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1YW4ugRQTl-YF_ZpLO7gIRSlHnB2Cwslz\n",
+            "To: /content/bigearthnet/datasets/bigearthnet-medium.tar\n",
+            "100%|██████████| 2.72G/2.72G [00:44<00:00, 61.4MB/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "../datasets/bigearthnet-medium/train loaded successfully.\n",
+            "../datasets/bigearthnet-medium/val loaded successfully.\n",
+            "../datasets/bigearthnet-medium/test loaded successfully.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Number of train samples: \", len(dm.train_dataloader().dataset))\n",
+        "print(\"Number of validation samples: \", len(dm.val_dataloader().dataset))\n",
+        "print(\"Number of test samples: \", len(dm.test_dataloader().dataset))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lAQy1KVW5y6J",
+        "outputId": "f4f21095-0830-4fa8-b302-31efa324235f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of train samples:  25000\n",
+            "Number of validation samples:  10000\n",
+            "Number of test samples:  10000\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# print all the class names:\n",
+        "from pprint import pprint\n",
+        "\n",
+        "print(\"List of available classes\")\n",
+        "ds = dm.val_dataloader().dataset\n",
+        "pprint(ds.class_names)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FRuegG18wTJU",
+        "outputId": "a9eca98d-99d5-453e-ebe4-76c5b8c80e62"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "List of available classes\n",
+            "['Agro-forestry areas',\n",
+            " 'Airports',\n",
+            " 'Annual crops associated with permanent crops',\n",
+            " 'Bare rock',\n",
+            " 'Beaches, dunes, sands',\n",
+            " 'Broad-leaved forest',\n",
+            " 'Burnt areas',\n",
+            " 'Coastal lagoons',\n",
+            " 'Complex cultivation patterns',\n",
+            " 'Coniferous forest',\n",
+            " 'Construction sites',\n",
+            " 'Continuous urban fabric',\n",
+            " 'Discontinuous urban fabric',\n",
+            " 'Dump sites',\n",
+            " 'Estuaries',\n",
+            " 'Fruit trees and berry plantations',\n",
+            " 'Green urban areas',\n",
+            " 'Industrial or commercial units',\n",
+            " 'Inland marshes',\n",
+            " 'Intertidal flats',\n",
+            " 'Land principally occupied by agriculture, with significant areas of natural '\n",
+            " 'vegetation',\n",
+            " 'Mineral extraction sites',\n",
+            " 'Mixed forest',\n",
+            " 'Moors and heathland',\n",
+            " 'Natural grassland',\n",
+            " 'Non-irrigated arable land',\n",
+            " 'Olive groves',\n",
+            " 'Pastures',\n",
+            " 'Peatbogs',\n",
+            " 'Permanently irrigated land',\n",
+            " 'Port areas',\n",
+            " 'Rice fields',\n",
+            " 'Road and rail networks and associated land',\n",
+            " 'Salines',\n",
+            " 'Salt marshes',\n",
+            " 'Sclerophyllous vegetation',\n",
+            " 'Sea and ocean',\n",
+            " 'Sparsely vegetated areas',\n",
+            " 'Sport and leisure facilities',\n",
+            " 'Transitional woodland/shrub',\n",
+            " 'Vineyards',\n",
+            " 'Water bodies',\n",
+            " 'Water courses']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import json\n",
+        "import pprint\n",
+        "import pandas as pd\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "from bigearthnet.datamodules.bigearthnet_datamodule import hub_labels_to_onehot\n",
+        "from bigearthnet.data.stats import compute_class_counts, compute_class_weights\n",
+        "\n",
+        "ds = dm.train_dataloader().dataset\n",
+        "\n",
+        "for dl in [dm.train_dataloader(), dm.val_dataloader()]:\n",
+        "    ds = dl.dataset\n",
+        "    n_classes = len(ds.class_names)\n",
+        "    hub_labels_list = ds.dataset.labels[:].numpy(aslist=True)\n",
+        "    onehot_labels = [hub_labels_to_onehot([hub_labels], n_classes) for hub_labels in hub_labels_list]\n",
+        "    fontsize=24\n",
+        "\n",
+        "    positive_counts = compute_class_counts(onehot_labels)\n",
+        "\n",
+        "    counts_dict = {cls: count for cls, count in zip(ds.class_names, positive_counts)}\n",
+        "    plt.figure()\n",
+        "    ax = pd.Series(counts_dict).sort_values(ascending=False).plot(kind='barh', figsize=(14, 14), fontsize=20)\n",
+        "    split = ds.dataset_path.stem\n",
+        "    split_str = \"Validation\" if split == 'val' else \"Train\"\n",
+        "    _ = ax.set_xlabel('Instance Count', fontsize=fontsize)\n",
+        "    _ = ax.set_title(f\"{split_str} Set Label Distribution\", fontsize=fontsize)\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "gT7hafdBwnMQ",
+        "outputId": "700ac28e-7c23-4c56-cedb-d4ad155089cb"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1008x1008 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1008x1008 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "def get_label_names(onehots, class_names):\n",
+        "    \"\"\"get all the names when a label is a one-hot.\"\"\"\n",
+        "    label_idx = np.where(onehots)[0]\n",
+        "    label_names = [class_names[idx] for idx in label_idx]\n",
+        "    return label_names\n",
+        "\n",
+        "def display_image(ds, idx):\n",
+        "    img = ds[idx]['data'].numpy()\n",
+        "    labels = ds[idx]['labels']\n",
+        "    for band in range(len(img)):\n",
+        "        # normalize based on min/max pixel values to clamp ranges in [0, 1]\n",
+        "        img[band, ...] = (img[band, ...] - np.min(img[band, ...])) / np.max(img[band, ...])\n",
+        "    \n",
+        "    img = np.moveaxis(img, 0, 2) # C, W, H > W, H, C\n",
+        "    img = img[:,:,::-1] # BGR to RGB\n",
+        "    \n",
+        "    label_names = get_label_names(labels, ds.class_names) \n",
+        "\n",
+        "    plt.title('\\n'.join(label_names))\n",
+        "    plt.imshow(img)\n",
+        "    ax = plt.gca()\n",
+        "    ax.set_axis_off()\n",
+        "    plt.show()\n",
+        "\n",
+        "ds = dm.val_dataloader().dataset\n",
+        "for idx in [0, 12, 311, 776, 44]:\n",
+        "    display_image(ds, idx)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "BRBExv0VvQMP",
+        "outputId": "ac5f16af-3a3a-4d49-8548-ff55b8c24c37"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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DVEALw9NSa9/z2q0BQWieKw7aBBDKUtFSqa1RqiAjKCE0VWiFKILFICIwc8ycInN6YIJIR4N3x/rO8MBUmeYTbZ64vrlKK70ONFY0OqXmfYsWtBauTzOX7QIumDvmziSFIsqyNARhNUHyQoQ3rBuCocVBas7FHRXhdFPQKnh3xnmw3Q/CBqij0xWihXx4hseazw9HhiABxQwNpSxXoDBFHAawvhXhxO6ID0orac0B98D3IB+/IAWYSC8S+f054EOIJhQho5UihBUk0itFBD6M6OAXyQU4HR0WfngZBco1ooHEQCQoCr7nOD/WV9GSino8H8bAB9DzMykCkyBCrs0QpFRCFDMhAC0ZmeFBSHpr1UA1KEUQVVCIEMKE6EEMKFpAlWiG1kBVUVfEBZWBSlDq4UikIlJRqYTAMMNWx1fPaMQD10BxzAc6BN8llz6S6zDevm/hhcpZ9ArBqba/NTpUCS3YDu5OrQYSWCjiEH3HvePaMSpKYXo0obUQWiDAzDIqkIFOFW2Ndd8xN6ZtRUWYlhlHCFdURi6qGViUOs9oaTSdKDi6b6gXai1wM8PSGOeODeNyvqdI0OQqH2CMDMFDGfcb+9bZPXApLNc3LKcTr7z8MvvdxrqdKW6UMWhXNcNdTizTzFd8+FWe3t3RN7Djv0elMheFmrkxm+I4FmdsKPtlhhj5zDjhroSdKU14/OqJsQ/W+5XtjXvOn7nQXlJ0KdSrD6JSkfMdERuDp3AssNKVYkobnaJKe2lGW0W5ytTCGr5veN9hBOGdMgkhQe+dGLCvQSAUKrQgahDiqdQF3AXfK9IKVYRalTop3iUNhgluQlwMXyFuFW4cbQbuuJPKIAVvj4hwVM6gHdGd4WlA0QlKpdWW1xcn+oDe8fPAzgZXJ8pcKUtGlmpHrtpmAhgjEDW0pQMIAUcRUYp4zr0BVVMJDcLBL4MwodWJqIGfNlQKqg3ZFTGl1DOlOG0+odpAZkCQI2XYd6PfG/02cht7ESiOMXDfYG8I6VBUE1sxf/u49sVhbVVUoZaKezCGETIIBtEKEcLohTDH9h0h/wZe1IBWENKy1CKUArUKroJNgnnHbUX3BRmaX044wkBKxWlQgmm2jGldEZ1RFaZJKSpMBEpQxAkteKlIzXDMMdwHWhwpzkbPPHnsdN/ZbXsr7F7mStSCLAulVPzc8fVMjDdBd7Q54YoPpdhGU2Fp0Jty1RbG2DEXTlpoqly2gRlHvhHo5Ox95c3XnW3sdB9IcZqAFENbpU0z4YLHxh7GOTZmZqoUlkWoEsi+EdvGfrdSpkqbGxIJuMuygBQudxvIzu6eBqw5y8k5vaRc5sbYBYMMSbeOm+E9Q7HeO2UpaFO0ZiQUAlIK7XpBrwtxPWA6QVso7Ogw9r3j4cj1NapG2Qc6CVEr+wasjtb0EG53OIFJx4+w2FwzD4xAw9ECFKgTxCroqjgVXHHzDD3vSkIf9x2VIKZOqILWjMy8Us3RcGotqKZBKaVR20y3QV87Ms5gnfAFIpVQBMQic1oryHDEOlIV0UIt12hR0O3IORtVlEqlLE4ZndKPiK9BiOJSkSJocdwMG5GG3T4vFvROlDOts0w1FdMz/Q+MaBMeBVYlOvRtUCQoDZCCtALUI1k+DFXJfDWa4n1njA16O3CfQCUQHUgIEQ1Vo01p3WMoUmo+nKZUhcnzPJHAixBRMiyTIMLwMFQD1NkZqDthO31sbGPF9h2GMV01ZJrweaKEEusg9gtuTxFRpCoRSgyl9Y3alLkEe1VOZaJ7MCyYJT3L/Z4WnADRDMX6ZaPfrwwFV6HORingxZBaqG2id8cDehhrdIRGCOgUqDqUTvjOfrczX2UohwShgkwLaGG733EPVhu0aUaulZuXlNPLimpl34R1hejjAOwOgG7d2S4rc1uQuaJFE6PhQNvnmXIV+DKIKTWnDCccwlZCAlmuUHbKpSNNiar0s+A9mK/yHnx0XAKrgYsQkQCNBdTIEFAKSIUygVvmhAcyibsRFnDOcN7XjojhcYHSoM6EVwileOZ5RdPgtFNBS0PLRJwPtHs9o7aSFqEhVhEBzAkpOAXGQKyjKCqVUq7QGqD3CIFGosAlan5XV4Ny1gQna1YSnHJgSM4YAxuDvgdub69/L1RO2zdQoYSkp5oLJgUjEA/UM5+McEpzVIGm1FKpzJSSHg6tGNBXJyyQ7unx6inDId/R4pQC5SZDBg0F3xm3Txg+YdaY5qA1I0wSICqScb9eMVblcu8gO0jnsncsglYqJsqTN8+0gMUDl0pM1/gmWHScirgSt0bYzna5Y2CE3qAyUalYc1AnFmFU4/bTr7P1BChcBqbOZawwjPUC5uBzpZSJRRZiGUTt+BbYCJa6Uwq82VNRbp9+mm0dXLYNG4raCXohtmA8uUVqQfUGOc3MH6y060Z91Ihzgb0wXVVEgu3ylL4NzncbVSf6mx3dFur9RCnXTCi+v4H0wURHqjOuhCGKRkOkctQ/gEDGQm2VR6/MRMl8qeiJ0hb2CMwE1x1UaNMrqEzwskFVRlU8HLeNUbL8sJ339JazHpZLwQ0pjk4gKvS7Des7lzdeY++ddRuoCCpKaQs6V6wH2ICxpocfhxFbMpQOF0Irro0qEwWllII8s+pDYUxYXGOjIqaJ1G4rEkqlJNKPEHOAKvXmmmmaWdqMiDM40Wrh1GbUJ8Qb6/wY0wmNnTAn6Ol8PBHk7sbYnWFCqU6p7zKsjfBMtr0gCqV9Fo7HdwgnBNBAaj5YbUJRpUahAJq1jUT1uiEeFB85VtuxJUOGMlqFcmqZX23gw/G9Y670KLSWSXTkP58t72jN5LpDMCA2xnAMaNoIh23rhAhNJPNmKcf/FY+AEYgNxAZjveCtEHWCaGhUTLcsWagQBP18Zngg8Sx+hW6DiI57xRPJylyHKQ1JcXTEUeIYaAl8gz48a6W7Y25ECJrxEITgfcejUcoNWgv1yilXBb2q+F6ILke5wvFI8MF6JxDYd8Z9YbQCV+k5iicQVCS9eJmgjEJpnpFCHKFWCBKNIoVprjgwTBDy3syFMYQQR1Up2qAGdU6DmCY4/4iYH6Ux6yM9ST3CRyGNgDiighTBNsMug+3TZ4YZ3Z22aIbcFbRmGhQaWVtDCM81IOH4kUeGVjIjzddRDyOOcoao4qVlecoFCMIN9chISbLcRIEoSpkmapuoWgDBIuvZc6tEV9xyTWsJKA5HiQlI4Mwd55jfOEDpF9CAXqicy+PTkacNdFb8Rom94L3gT8/0beWoODEvjakq10tBR6V0sHXHYzCuZ0IEv+wUhbYUoihWKrJ1nEGbJ6a58oGbiQjhjcuFMTr7mHAtWIFRsk5WNR+Q9z2/XZ+YijA/VvZeGaOxXQZhQTkFSOe8XjCF0oR9zPSYEHMU4+6Np5gFzS/UGnDaodxAu4b1CfRz1nOLcv34IzRg3N8yehayd4VVwEchXFhOC6hwLgN3Y9s2phYsU6HXNG5xLVhx7IkSm3H7vbfooiw3BX9ckaUisyEtiFODMtHiioLBQgJCXRLRtcF29iQJ6EJZJq7nKb2HK2vrxDDaqhSdqF2QmJjmx+BOH50Fo+hAtCNsmA8IYVmuaHOllWBfjf60U6xDf8r9a51+NpYPFLRWJDaEHdON2CfYC4oQDUbfjprqgFIRmbKGqEYxkFBandFaQBOUKldZZlKUcpopVxO6NKQpy3WCWj4q4oJYydrscGIN7BLs10GZnFYG7sLltY6UoMwOUWlR0HJDFAjdYAzGsCzd9QFVkFZBFoTCTMmSmu9EOLENtAjzArf9zP26s62GD88Wdpes+KpTy+XQKqHUdFrhygvA2i8MCIU7RBqdLKICFsRwvFuWOlQpMlNUKbXkwwzBO3kuiQaKHK+jHKOa3i8QSinUUphrejp8Ty9JAk8ckHxEWm5FD6/LAckX2tLQGnRT2vkCYQnVE7h1RsAuwW7QLZhioGG4pSKrWsL9NfOsEHkrZ82dQhKAUtLCx1AsyLKSREYUoVlwVo45g43jOUV6uFKEw1AffI7MQ+oombdkdSnRSKBNjVYmJjI92P0wtyYoCdd7+PG88iRVPcoEhSi5VkoEEn4QNQQtlaKewNokaMjxOYGX9DY6NbQmEOMj05KwzPvc9jRaGSMRoxMx3toGQXOKWWqzLM4/K96ny8z1pKJHqa4ea6NDAZ0bAhQpyDwhbcr8U0CaHKSYzEXzfhMZ9mHE8Jyn5z0JwhiKuBOxJ8FAgjhQW0mIIDEL5bNkG+IgORzRicIYWRPN55Dr0ywY5tgwbHhGHRIIkd9jOco2kZ/BUQd+0Z4RL1TOMWX1PzSIliGsbR2/Dew88J0DhVWm6UQrQpmFMisFJS4OXbDWCFHmqzkXthZUdmqc2aOANZY6cZobLy2PsQGf6k8SJq8T4YGYZ45ohXJSqgrrSMVv7iw3E49efgWrgikgn2S9PyNzMIYTfWeNzpkdu1TGpfCBR8IyQ9NEFl96dUJqemn3liWfqaHzCRlrhtPjDqmV+sETcgd7F1xWlC0BDoIohzk8RyJz26BGwUqhVGiTpoX1wSSBt45dXTA9IduC7R0fg2k3pia8+kNfZrleOM3K+RLcvkbm7T2o6sRVcHHFPMh9ubKep8yUcsoSWBG8LogUKBcCR+vESYLHJ2Xvzr4bl72wu6GnmZBC6w0fndunTwgTkJqhuip1GUQMiMcZnt8+wYoQy0QtSj0554sxdsP2NBZlWqCUrGki4IVSThSdiCp4Dfp0wdWZy8uoCrUVuinDFNeNEEuvBsTT1KXSoPdgu3NiXWHb0OYoDT+dCKl4PRH9TL+7J1QIUer1VXr9fQAOE1AVnY+Uy45oTwKds8xy9z23iBnTtNFXON/Dfq6MC2yXwbCBXgVaguId1UqtJ9wCG45TcMl8/F17zr53BEciskiLEz1LHqqFUoVag1obbV6o9SguI0mX0wmtFdNCiFBaXi4JUopEJU1k1j/Dgv3sjG5EXzN/K3uOFiXCGCacLxd2FfZ1RwW8bqkUWmjLTGlHEbtmjm9+FNJD8dGwngSEMYRehNCCHHMkyLoWjsSGuaXP9AoE23bQ3VAua9AtCOm4WHpQoPcMe/ZzIqfhULvQuiKzMBfFrWACtYEprJKgFG6EDaLvMCXc26aJeVqYl0rvgphnzbInRfeZo/YAiwMldqW0wrQU3CMVt28YwugjvU/JmuXVVaWOoCzQby+MfUdKS0JAGBEDi5E84dPEfF2ZF6XNWxIMTHE36BvuJVlLBaKk9/Jh6UnQtzjFSWM7aplqoJbfkwWmTpRUkmTdJS4hHgcan6eHAz3DWysd68Y4D9SzziklvaONkfVOX+FZnZtn0+hJ83vm1et0lEkK4g5iSUQoyZATyXo+ARTFIlgvG9vW6XvHoxNiuCc2Yp4U1JCCW8d9MLph/aADlnepnJfbe1SFNmlCy08HmKPu1PmEzIWpBrU1Ti+9hNBRe0KxhLGnMiM1CcQmQtUJD2frG+K5KoODcWTG2DtvfuqSoMHlDbwMvAxETygnLDrDO6+9cQsj8NugFOd00ylPT7TX3+SDL7/Mo+trzAejQN+CYTBUiVHwtRLbDvvGukZye+fMdboFpQflLGjZoe1cemEbhTrNqMDT0SEMMWPEYI89GS8KZgUPYZzvsD44f/qSYfm84JHF8Fdm5XpqsAcmTrsq9Oj02JGLIE87sa3YumLzQkyVZbnm6uoRV1fXjO0Nyv7d2CbYpkwz5MdlmLSPDJ9gYrmeePSRicuTznrXGecnMAa7Zxnmul6znCY+8IErNmmsUel8Gnt6n2wsdUTOROx02Wg3E48/9IhTnZi10s+B6sr2phJjMMYtlIXgBp8NU2P0jq2DMs2IVkLnA8jZCDWijsQj1Nj7wLozihE1kFAsjBg7OgTtgk4QVaAnqOKrgg5GvbCfB9tnBsvjzFfLJKhC33ZCdpxzorIyZWTRQJJNgzAj0mB5nMCUJJFb2Sltok5TlmFU0ehpDRfY3Vmf3LKfV8a64i152LYVIum9lCKoF9xXzM6sd0bfnNOrV5T57bXzxTmng6owTxO2dfbeM272kp0OlAQkxBA/E2OwXnZO4kwalOkabRNl2yCCujzLXQPbg7EH3jd8GFsFM0W2ckDrWfxOVpOsivgAACAASURBVHIFFNUkLuOJEFpzQoTuhnvCtXe3d9hl56LQJeuv5obaM8MobAibFkbN0KY8YyW2rFVNMiVZooKaoA5tEkoTKle4GeendwzvjLEdyiBYZP4hkfmIloNoTdK0uhl7D9qeIZkKnMdO753tvtOiMtfGdJqxWtGqhAu3r38Gu6zYBzuX7UyIMGywbwMLo3RnY8KDRD2zio7tg/5kEPdGWT2R6VqzZqyFUgchynoJLAbiQTNnLlBqSWQ6FBehdKE5tOrE2NjGxrqtbHvPeapjzFAnZEoyfuhBfyuajB01Qnqi2FoQAjfBNHPVEUeUIpmQi2V7hKsiTTgo24RIkuUBWuBjMN68Yzzt+LnD1RUiC7XNlFoI2TKENEfqjOhMKZ4ElSOufIYThPXkSNfjCyoTOitlgTIFRR3mjMj6lk68kPTF4ZGhtiQLLrICkyi6rJmPux9VjYK0BvVdcmvFSa7oPLP3YO/xFiQSWtLrjQAMtXvGbqxPN9pk6OKU8gidJ+r9jpjTFohyMGbc2c2xvuL7yqot61B3CXJw80w5p6ShhSKloJKQdahjk+EYPTJ0Iwa3t7dcumDXN3it9D6IMISgiXI9Zf61TcqIiqEsml0fUpPXOc8LQ7N8pJcMeeoitEU5tRv6vvP07jPY2LB+YZhipsfjFJSOhFNqKmdIMnI2G2w9qJugc3JDz2NjX3fW1zt6UtrLham2RHRtJ4bz5HtfZz3N2OzsW1pt88G2nrkMy1KWKCpZO0sOseK7sb/RYXdqd8ZJEdUM0RVK64ByORtqjo5BNWPWoNYc210xlLYrLWCqRr90xnmwritbH1y1mqkPC1IaMiffOtlFmp02zROFpYMoynSEquDRMR2Mo36tZU4QxgamSZjXRqaoyqH0iaZFC2zv7J+5xZ7sxN0GL8+oTLRpobZCyAXzgW8GZaLIQpGBipEsgCDpWqmcz9YapUCr6KLoCcrsFFFiIemrG7QiTC35vN3jWAHJsyWSXSfihKw4HSzQolRVaBO09u6Uc6xGoVCjQe3YVbBeBtsa1FmopYIYHjC2S4aQe+CzwtLYJQgf6DIo4jy+SeL7/VYZW4fVCQMTxST5jwnlJwl6DGU/J/jTytG+UxNxVIJWywEYBFjQu9O3DemDtthBhk401YcdQMJglEJpM7ILYlBiRyMXgdNwq5gPdowxJAvta+BkyGWATguFwuSFooFJ1sQxEKmp2FOWM+ooIB1kpTNx7kLNRib2bWf4SP7xPLHPMzo6p9HZNVuy3ry/pewX+mdOeA+2e8F7OXL6QGpglkUtLTOlFKa2EN3Z7s65+CJQXkJ0YpqNUgNdCuHOfn6D/bKxXzZGPeFlgq0iImx3J/paUIIYM/1c2e7v2e/vGNtG7IHoTUYzW5atGLdIK9nOFhW0UKohNWvZRAGbDtJ3sHswPFlAeiD6EBhCFKGckqongA4nenomW53tEyveN2QvtOma6SseMb/yEvX6CpkXpBZO5SXMDkNggY+7xBLcKNeONPBdkTg6T2oS/CtCDaFJy5B3PyUafNmw7vRYKXVimhceaWM+vcxgw2PgcU50mgRDh1cYhehH7lo0wVF/l8R3252oUMjG2zbBdnHcnBKGAuPoHsgkF3xkruxFGeH4GNTmtBJcLalwl7MiQxLUIEkBrlm6qOKIOCaChTBWQWtaSI4ygB5WuaomX3FyfM+ui+GG24ZGliWCmqUBM0Kz1OGlJDg1sm9RwiCyFGIIZmDhjHimnJqdGyqMOcnKWqfs4qgg1ZOgsBmhkZTHIpSrOYveW3oHj4FFZx+VkASYxjAigrZUZGpYq1TvFDGsZE300nfEBuV+RUb2bYYJKoVaoJSgR8eJVM7aWOYT3VYu/Zz5kcIkEyInalspNZBaiX3DtnvWy5nz7T1y9SFknik9+y/7WrC9oSxglbEJfTf2fSNG50im80u3JBpI7CANKdMBAuWClxbJfQ4l4oi8yBzZyHLFs38P/kUCV1WPqkYge64bW51xGfQ3V7BBrYUyT9SrhXq1UKYJqTWpkeWEStAFYMPsPjEUT/IETYhNwbKkIzllqsAscvBna9ZUR2JIPpwhg6BSamHWRpsaa7+n205tG+aeKSCFiHooBlCzbzif8LtkCE1AxUFWKIEuV+hqaPcE3TRDKA/nftsIU2RuXIbjT3bG3S3RBx/5UR9kuTkxXy9IN8ZlhSthjkqT5FhuMeGhxNWFbD5uSdmaAiXrVrZ23Jx5vkalQk/eqy4nYjbiejDWBetGnefsqn/ylNEHl8tOmSpTm2lNaSeljxXzwUUCMWP99C3sT/E3d8xWut0zzhO+V8aHk5vJm0aplaurE9Eqpk53TWJ8bETYZ+t2l0OBpLCbsY1B984eRz4CqE+Uqjx+ecECduuUKEicECqqgh5/5KD3gG2Fy2cyjG1KXU5JgPeMEmJuzFPl5ZcXtougp+TojAiua6WpcO490eSnQeyD8WTQu2JxYinZNXH79LUkgVz2RFpbYwy4fKYSTLTpMadH2dMZAwhnuS5JMJiXY/cFTX50BNOrN2hVxt2O9aD3gWlgJRH7kiY+OdH2rEE6IyK/DEKy86e/ObB7Y33tjG8DulGaMr30iDot1OWa5VRoixByT3dwn7DuXM5rxqJ+psxX6PSIejWhrUAvMBz1lVIL09RyrUwn+jD2pyvmtzCMfn/GBdr1jGvhzjfEHHHLRvUhlDEhEZik56QIMglIQEueOC3ePUNIj27wJLt7UtL0SJ7FnlXQMxm2pPppybxhDLJTZd+TAijlrULtGEYQlCaolqMpumaDbtOk+EXC6NLkQN0jbywiraSWhNI1FSBUE3rHoESWAjyO+oInoyPiCJ1znnJ0P7goBNi246vR7+8x2zBb8dVhNHrP/APZD+ORQInrgRqOJDG7B1UyPEpWXxw7FZCtVRHpQY0Eo7TRamO5WhjD8bVTatpqjZxXaS3LWHbci+9oyd0XntFTi9a3AB9tJWmUVqjLxNHZSA2hRoIgEUc5ZkTC/mQfLAdSOfYz+77RuyMotWrWfTejNEFqpVTAPYvukH2itcI8c7SDIiVAPJk2qtlSuOca8GJZMkGy2eHZwju+Z+Sga46DRieG74Ztlp1DezYPaFW0NrROlNLQmijtiJ7k/qhZXxw9+2lJoKq0muOlHiUNgSFHy/JnSTP5nIxhO2ED9wF6tJOJYJG9tOLpCVU117UfGM1BZkgQx5OEcJApePumlC/Qz3k1ITOs3LFvcHmarThTKwyyHy32FfeBmVFroV1NtJhoTLRXR6Jl9cT9pXL3PZ+mb527p2faVFhOE+3RQrmqTLvRzel2ldzTS2Rv4XVki4KSaKgUbl46URX63ZPcU+iS+V1drolpI2qHvYMZczhVIa4rdZ45na4/2w51WtAl2FfF+8Aun2b0jUu5xzWwmuRPccFeqpSrwqJOLQPRHds7d5fB5XbjcrdDzYjitLxMrYWxdYbvbNuWqKQll9VV2W2mSOHRdXC6rnzoBz9m7Mp2KxyNNfRPvcl+Xrm6EQLF7gwfYOUaqxO0hWqGroPWbpAyYTTEgtu7FYnCPN2w1KPp4o2OrTtjzz11rq8avcJ2vWRvaSi97Jhd6OfX6evOiEdAwfaNUjs2XZhbpU0Fvc+OpLt9R0S4vnmMtgmZr7FtZ+87OjtSYezg++Dpd73OGDtbGZTrkr2bIxuds5ld0OkId0lW0rjPjicVRfdKGUqTikzK/MqSTKdYKCTjSSRr1vt90vHUJ7CgDEeLUNuJVmdaaZTItVBuMv0Z65JpkxljrPjlIK5HYFQ8CjINtFTm+hgpHeFC2EaMnXazoK1StoYPZ79L0HLEipFN7WqgLmjLCOtdKWdoshj6xehn6Pef7SZ/1uwrqigJpEiteUE/di6Y0qLt2/GnONYLbk6daza91qCWQpGGRm4UlsXgBMqkKPEsFBBHPbemOE0LrQjbMHrPXQhyv6C0vckYO4jV04yG0dWoU6U0PVqC4q3cVSXh+WTfCdRykL6zLUk9lU5blgUIZz3fs++D7bLR9x3zfpDY5CBz89b/069oLp6SyYxETZ5w6RQVii/ggduAsOyIsKyptjaBBHvrSR+3mgRrJpALLo55P77m3H/IPZ49tvxceUbvg9ZKEhDmilsgVmGA98COfYE86kEDzPDiWeuctKS7HZBwAk0HJVPLUdtbV/q20/ctSxYKsV+w3ej9kuT+ejwg8bd2PcCSiheWTywOIDV3TsgGDBXLEs8yIRJMVwfryY6NulqCST6CcXFs98x1yXvRoulZi6Il8YYkNxzMp9beqnHmc7SD7pgrSwCtJQ1CLUcZkbcS5FIKtVXwmgCiHtHPsWMIB4kiSRHx7nPOLsLoQf8bO+Ms7G8W6rVQr4BHFWnKxCmZs+qUpuhVQ02QPoi5YVq5e/0W78ZVvaOdZl75oT8YtR3tZ0otlFi4u9zi+0q0gorQThk6yWkGdmCnrEq1xoduHjNNE3ftw2zbRuhrmcuNy1HvhMKUBIpXTrkxl6xoEaYquQnTFpQ6EA3seHCjNaQUSp2PpoJA64rEzjyfmKZCPTXGpfOpj3/y/+PsTZYjSbKsve/qYGbuQERmVVc3KaQI3/91uOeClH/srs4hAoC7mQ73cnEUkb35S6RTSkJqkZkIwGGmeodzvkPvk9adWGP3WzVqTYS5wFWmkbxTKRxUbuw3KPuaneAUa+SZsG8/ka8n29s75/jgmg/82UlufHn9F6zC9/SvcAUtHdQ4OPwG5Ynni4+rA5nj9ldyznLhLxN8dHlw8Z1UCj9/zeQtw3EQZybNgbeT/rgYaPkf+Z8kP7tpQGIkOWJqIZmUPO5a4N/KppVJTrTz5O2377R+cvWL/Z8OSlRm73jrnPE75Ew+Xkl7wkpAbwSTuDYNi2xq19lVbpYDUi/kvmG548ek3n8iJeP+It7QmJNSJZgZz0F/Dq5/68zLKf+79or5JldL2aDuULcmmkNPWGxYzpry2iTiIWXVMmwnD9Ii6eX9Rs6F7ZbwYcxLR29KGsTVo5LdmASjaj1TZ2LkSd8mAxMMoE/+URrKP16ljI7NSR5Ti/TdcXPGyOSh/k1moMC7hL54kFKm7IWUncCZrsFLd2TNmcYcRrughlPKIBfYbF31i9SGOfOSsz+4ICqY8zw7owfn98HwQQFmSAEU3gmfeM5gif3QSZi8MFlyqliCaRIWkgkYk5xVDqWlSDdLmFWSG70bfgVRBmM0WjRxdyKTkyBOVivsWc6kGNhce7TZyKVQdzhuiW03ziGrWqLhEby//Yq3i35+0Po7vT+4hthGMSHlRK1F3tnTyB5kBj0ZMxemjWX1CrJlXuqLbqU5efbJ2Scba0+HMWbQ3i6u50V7+6CfndG7JIRha+0B9b6E/LYJg1IS3gY+ZNVyS6oMPIiPRjs7/RxECvKmqTWfBoeUSLdDRolbEWPItGKxJd9UAyx6nwr8RXnsQ+us5wVjUl9v5Jopy0bonv7o800AuHLfsC0od5HxUgrxj8oiPRStMgIgDSwHZVP9MfuOZvfOJ2kwZfXBOe2UunG7b/QWnF6ZIxizw9uDdIqeEJPVc8YSNBRdOB6LvaTP7c/dnNepb34MOR9egzEKqadlmM5ij4YxLyEK43DyrkX+/NQS+sWYXbjKkYjLaA0eDzjmZNub9rE508sqmYbhfTIfJ24Xnk68HOS08f39JIfRfvmOZahfd0YkvBd8Sm0yqwgOt5dCJjiuwXNOfm+DFOU/qI+AeGAYNVfMJp2hMm35WInK1ZImh/litotmJ1gh2U7JO2W7Ybc7sVcivYEPbK1ALC7yUdl/Cl5uiaMW4lnoMySY75Pffv3vjDG4emO2d2Z70GqGUvEWCxy2YUzO4pTpFLu4itFrxU85bDD9HH85/ononRkftOuin5Njc2px8X1b8O33B+3x5Pnbb1wBPYzoG3jm+CkoW/DydX0P6YUC7Amu75N2udRPJn1pzMB/P2m90x6D/GrUWyVtJuH91KFQvrxqzfSSVTqmC6uQk3yWPiH5xMLIaflTx2CcnfkBvD1hTI7XO7VktkOcYm8L2TnVm1Il6CgB289JPXe4eMIpk+rni9I13CwNK4ntSNIHzxv4EvYvQFqqQwICe2HfNr58PXiexuWN+XHS28nHeRIBL+WQU6ZI5+05EVUrPB9PGIP5aXb6My/n/bWSIsheJdJOSaFxDTw5M3TjYEbO6qGIoglgatKghnO73Rll4v1Jayf//m//g/7onN9OfvqXn0l/feXl60bdN96bTsJUM4MhxsoABrQ2gZP2MbS8ff9OrYXX+88Eif3IjCuY3eE5sQbsUgS1LXM9nPOtU+qkbIk+sgYhbcjbaTLn1rR+iRTCJbl6psANxlWIHpT4oinhVqVDLZNxnli7yPdTZVr0ZX52jeOvwUcLmnXm2HU3fMnMBFe7lkDfsHKnpF07wZyY14l5p8xMGYkjCY3ZvBHNSLOyb+rtrGTcgj7e2PbE/evOo9x4Juf5+4OPaxC/CEEZeaOPi0aXQ384ydeOuFQN83olRSUvkXzNkj5OM2YLZg9mmbodDt0+2Qociairj59B+xjMy5nvhu2Jekj4xtx1k6x2xICUFwiuO/Ny2ofjj0k8J8m6yHouOd4MTb2jFn3mNjWZn07J4ipZXlPSQPY870sskkTem1KYmSdqaOc+t1hQ82v1qYmZsozi42SmTjmfgnr5hJzIx0GcTczg6HgkxviEjK1e+mqM+DRJVG0Z/szLebsVDKNYYpgwD57FhT39yYhJZmqnl6XDNU8aX/ehEboF+36Qi/PsT3pvfLy9Md4m16+d/b5xfN3ZtheOe+UaE3eT2zwk+7LpMIPRgzka3Z+ED+r5nd139nmXGbkW6AASJRgGXWjEVjPNG9ejE3eDzeizMHui9EFySHUXoc2CkjI1VXw5LmxhsGdP0AslXkglyIfJfoTj7SI88NogScTgrNN/Or1P6JM2jIqRSib/lJjJaWOoPEyJnCXEz0WC+ugNn0lUglD5P5h0H0TfsJnZv2xCa2a9nGM+OMrO/S8vHH2jtsHHv/7O9f1BuzqWMy9/+cqcncFkemfOhkVeD/QXUl7IEjQHyGvggS1IWId5BTPLXZLr/DFEij3jNQMT88l46s/8lkl3I/1tafF6kjXQtXKSRdPAnTEb43L6bwHnJK6Gvc5FkXN91qEbnJzl5wzxo3An56JBTxZGxcL038xB8qSXaCa1WSGfbma1VUUl9/SBlSqH0+dScjSmwfvlzGEM18+ct4o3md+DAW6rv5SGm+nEmMzseAq9fv/AlvIPX86//u2nJdUo+DBGT4x7Y2xNboA5yf6mXafp9JzJ6UNcm7QZVoy6C43xuDZmkg41fU3cj0L66YW5VR4fk3k5aVYNcl6SaHp+sKVMOgq1mSxa17t2bikzU/D97QPySZSylsGV28udnDLfvr8z+uDj9w/6nPgYXD1xXYnx/kH04PWnF339amRLzFgl7RQYu/dB3rIE8uNdNqjatN7oGyltZKtYehCp40sRsrBO5JRI88CflT4k8B4vJ7YZnotMukmlXd2MmgslV5XUFkw7CTqPob6yeMJsSJlUCpYPjr2SSqZ5xrvzy/Pk7b3x+y8P2ntQHsHNN3JdhPYNyv9WiBNivJLSyZaNUnZSKrjdGTOTQ7awGI1nN55vmcfbG+fHg3YNfIbAyNnwLWst4iEddM4imrsxyPTkpNsT2wvmRbfXRNNpJgw5a66ndpjtm2jz0aRTzltm+1rl5PAkHtOHQ5qaukasvaYwNOKfBzkvq1kU5uWMc2A1oE7i07I4Ltzh9+dTqqxz3dw9MOuYD1pXS5xG1iCxywNqOPl4pWwv+K1ixRYRYXK+v8nscQA9ET2RfsrYnhFI50/K9/Z9gzAxcTDSAMsDMyOPoqY9kj5c7S/+0NoOU9nnxlZ0q4ap/qYEqVTqsWN7ZSajXRO6Ygkk1/Jllk9rEpgpJFg7okiGLRdIbx1PgxidQqVYoWyVUjLPbyf96ly/P3FDOsphRGMpTIZAxyV92kp/fF7ugnHNPsm1aIMQAsBEUsyDDw0ZUs5izaT1cEQio8GTmZD98j3y4zCzJF2xL7y/Ja2QckUP49rFxPJpjjFJJIqZqBSWSFXL91p2Ofq74gjOs9OBZpDOTGrScuayGEGbYTfAFxw5F4oVcq2kXGHxdwIRKXysW6IP2tmYTRQEDzBfDpYVW2FrX2mWfghBJNM0bJsaxKz4hbX4kMhgiS7mKYP6eEhnbabnpdRCPjJlz/rBUMmsj0831R+av/RjBfepNhJHSBLTabFIHMvptAZPVxNM/3qi7z0QqCvkpJoTsuvZDO8aNqZBKTdiW8IJ08BtdKefly7IInojvWgttKqt+Ny3/WdfTv/Q/mlax69JvDciLoJGsY3YjFZCSMr2Si6wbzA9070oPsGC3N9VpvweWEl8/etGqYVaC8ODazR+e5/kGRxcyp34sOVIkKfvYpDthUTltn3FGcQxmX3SvzXG2bmuxq3cOYrx8r9XXr7emD/deT5OnlO8obzX9cIYYy+ET+rPO5aM9vZO687bM7BupJaZLqvPy9dK3Quj3hmt8fF+Em0Qj5PydVC+uBhLdWd8y8QMRrkkzM+VlBO5DPzytYMzykx8+WdlmVwlAYM5Omwnvl28lFeKbfjbF2guzupm5J839s0ou3Fsf6HkOx+/feN6nlzf/y4RfA/UdBTm7Mw5mKnhxcmvBUqinRft6fRHI++FcvtKKUU33iHMqFXdHu1XZ/YhA753kjtei8TpPqXrX3yVctvULxdjXK4Humotsr0cpFLI+wEOntfLGUMi+WnM8yIM9rsM/futUOpXcv1Kur9hVe2DEfo6BMYkhib2JN1Io3/gI2i/SRVWt8w8nf6Y4i53I24OJTBTkJb/tuETxjNINyPfBYtuwznfTmJO6rbpQJ5aKfXuPD+ebFvg1nAfPH77wMcgRcdsJ82fdbCnzpgDa8bItz/fc7prcT3c8Wvg7WJyMek/hOqfCrVkOgndYznvHVxUtZEXqS8WXygHJZsg02O59D+x/+EolEfSOC9iwDiuXao5JYNb1n7SEzPP9QuRxC9SCCblHaPoFN+0gD72qtPKJEGUMgZiOOMpuVp7BnQjXYYlYRtB/XPKGcuSyEUYbEEqRiz8f/inmFm3hXb1voTcpqm3TZVln1VNQE5LnZ4kupBYX3Ds2aZKrDnAlTVSMGpWgE7KofL7edKvgbtLn5sTVqrkgu70EHAs2DF0G/qQzs7CfnBycjLKlrGiYQvNRcSb+pPCFfy0fiaEmSL5kv9l+0Gucx/4HOuzM6zo+xK+8o9nLUw3eyQJBPgUspdE3SUOyLVC0edPKqpIUtYKL1A1ISSjfgOfn2NXeZsQ4ydc/08I5kUYNpoqGtaQaCxZ5tQkWuW6/xC6AHxubmaIE9VtECZ98JwiYOSqz4NIC2iGKiyDYC7nyp95OUtjXJOPf7+YrdGvpxpdc850yeqFpl61PpjNeO9GNFPZYcr6oJzSKO5/keonwIb8gWET8mB73cmWSCOICVdXGWizMNwZ7lh+QDL2n4BidL+rVP1SKceDcgxSBs+DXx+/8NYyR2wMB79Vjpr52/2gZ+FJ/LtxnYP+S2eeF+cvv9KG8z4MWsATjr8cbK8b11RcW80v1D3z099+/rEvHVNG2/k+8OEcu8rnYZvUOW0SW8bzwYgnzpMcCUbm/aOQIrHlJN5I3fRSX3Ceg2iN97dvMiIfQTp32vyZv3ypfE0wxjcu+8av//3feL43zlxIpXK/3aivB6//9Mrb79/pb2+c3x6MdmGvRU6fmfDm1DIEc+5O2ZxaC/effiKVwuPdmX5xPU4iOVQpiWKG2Ly2emozsmsNFlXumzEb7ToZ18BqxZLheVdf+PZQ2ZkCT1k6YUJas7pBTqTtRjKn5Llezo6nSpCp6UUpZCn41C97En3BR4ggnw+ZH069iPM6dUmkSb8GbTTsub6H+YElY7u/ynBAZVyn1h6+g1dsk2Y27/cF+2pYlp/WmUzOpeMOit2wDfZ/msQsjIex3TL73bj2RM9AP7F/QJX+x/K9H3fAlEQs2bL6LHc/sXgsYniCRtXyMrJ4MYHCd9RSeAqJAcJ0KplYMv65+J/rRDV5DaN3SJ/kdtHNnTUV7kmnGiqn8lZJadOEjgrTuHpnuK9TymgR9Cmj9/U4uR4DHk183KneuexlAaAM2wtUCQZ8ruGDGaUsFk7KElQv0zMJZlIZFYv6jmkHbH2qXwmZmUtOpK5e73bbGGly1a4b8GykS19jrKCk9nRS6sT44JE39rrTMXrA82q0OWCTAYCiduTsH1ztg3Z94N4lk1vUQkffb96ypJhLvla2Slmgbb2BS7z/eVOmhOcgJU0b05rK//jaA7GBQMb4jjiuhr7eFFZSBnfdpp89t1i2yxoYegJjmSuUzlf/A53Q19+5zCbrmXQ3Sf/UZWKLJeVdAoeYXX1kX1d30trEMuTo2AzGaZAGkdsfkRfLjODRleGzqCCqjlbvmtBNrBiVNX+wNbVXdz2nnv8fJMI/83K6Z5WZaeIFpkkJ4RNGnhpkuPZVPW6aWvlF3ox6ALaDFfyjrhFy4MWl1HDoIzPMmaYyIAGpNwzxQsNdD+kh/mykxLTMCIkZ/GErR0fayLLdl8t9o9qORfDx/t8UXrQleoI3d1q/aP3i+98/uN4axxzgU1PMWrh/ueMuCFfe5EV0D2abkJrUJ9umgyIbuTdmaoJNJaPXTQ/GqYrBciF5kM9T3FmDeisCFF8Ht7rzzz9/4T01/p7e+Xj7xvuv/072nRSZEfKUnt862CBvH8x+o407j1OEin6egHM7EqmCH52Tk4+3xvP7d65vH2tQ8+mhVLlNgfpa5DuMRDnu1NtO9U1ujHMQY8C+hiMkvMjBUuohLtBUKTf8IWzqFXhBa52W8CtBHTrqe4fh+McklQS7l9S11wAAIABJREFUbqmwtHCXTk4nNidpdm0AGASJ5FUhVqmQ61MKsil/ZX83Ik+8unrPmSkL8JVu6JBrLnxnf0AvIuq7DmK3oUPTJQrpv4Plk1ROUlJMSMxNw8rtQyFH40UUwVIJn8xwDdxSYOvzml3ty0DTZBtBG0Y3o9yLXDt/5uX89Rc1tf15qcdLShz2FIubA7XI9lWq+gj6xOqG14NqCqy12yZO0Ll+eacCjcbn14mymDBDIT+RfiSS1bp4Mf9Bdjcv3QBjjuXEmtSa2G8b0QRpvd8mJQXPQyP7iMxsicuH3BanbqcxBz0t21G9ib52e6GoKSClSTJf+73E6Anvi320uDFjKHQ2ZPSiUlQwOH+YhvOSj6WDlA/qTTre1pxSXF7TSNxm5sPfcN/W6e94Ve9t3CUzKyfdBm/PB9dbMK4gveiG6E25qSUlpk9a7/RmTNvIa5Lqq/y0S4ekEKQS9lvWxPV8f+Bt8Pz9ndaG0qGzrfwcU1WUA5JmARZO8cQydy0VXSyjvCgRMaGQlfW6b7KQpXVdMn7EbMTiRX1a09JxW4lfmbpDKZN06LbyszLTKilD5vkYAWNKv2yLmtAH82MseeDy0haRFiLZiqBw4oJoggSkKRbUpBE2xCdKtowHhRVsTZRQfo8CgSAl0ixaQU1VkvuXkCppK8QpYEEkyST/3Mv57x8qA/zC6k7a7wtl6LLOhBAoOWe2XRpQj4GXG15vFJzNhBOZY/L79U0qiTPhNTH3WD9dWnrWpd6ITPZMQgGqkdb+apUOczacyfUJV05GyjvHbaevyPF7HWw1+OWWsWb4Uzs4PzvzvTE/LkaXMiZXBd3krzuWd3J5UQ6jOTYvSRhTgciME8ydEY05xPSJRb7jLkNtlQdBRl8WS6eInVTLjVwq5aZ+6elNPdVWqCNxtDt5/krMjeHy0VIdUsb2G2adlC7loTw747vjz+D4epD3TD9lVt4zjOZcj0nHGGlj6Tpwz9o9Dic7lOHkO+RDjg0SnG8f9MfF47d3ujsjMmkTNJxSIJflsFgqsXDyJ8F8aEBoFqSq4cpUyJsm5Ukvp1aghtkE+g9Ac4R62G1bCqwvr+qJCbbdycWJHaFOe1UagK2I+rmEJ2PN2ky4ndkG/jEBgddSNcpmatVIMJVOFhfCwE5J96IPPCaOMZpanYlapxxOSk4qA3s5yFkHjuUM48CmMUcjH0796vIYlxu0E78Ezv5Hb+A/Fr7HIHzQ5wOLgZhKFSPrpSzGdqhHud1ecE+0/UOOhatzHJljK7z+5StB8IzB6B33Sa4beTuI1okuuHJKwbhpIGLpFHLz+MponXZ1MWCjU4sQF+HSdc4p4X07F+z6Y/CeFBLzuIw2k4TaPpjxhOLYi1H6jruRb7KSbV+rVCvnIJdQjmYkIjam6VaI4eSAo1a8wHbfaGPS5mBkQazqFxOlPL/ic9DHAy+VKxe2mthLUl7JcN7+xzceBvnxjo9MOwvnfJK/3LRS8EFZeMAe6qJyOhaQKmFfL3wfpG2Xob01UoZ5N4aH6IPpk5ao78/oWi3cslZcbYg0mIL+TKRr0J4fjNa5UtVnPFwVTtm135xDE3JDjv6A3NSfxkQ40ox2e5aXlBNFQxaJSqRyizXhZu0lwTYhYK7nxEYnPz/IdVKq0+3AqdCLDvrvH5Jepl3wMvnTMOsqVyOYTcyfQZBLxu5rTZTQJNWSZIQBdQsoxlZV5hOZ6VXVxuMpo3VTrGS52RK0mzA9b410dSwltmRYKtiXjJdMG65NRZpsPwfln4ztb0t7/GdezhmOx2R416jd2o+0JzMjJ+2vSlVWpPvAWcqNLjhYzYnb6wEpcTtutFxos5FLJW1FSPshkXNJQRQJHghN6Ort0AqmJ9wvgklOa0+p3bFQGdOk82wDvxrPxyAVaK5prcbgg4illUxJ+tkQ3VvIjypGzxlkJtmmYMBW5PBYYTSw4GbF8KrTkeEo1TuUAZsTMXZJ3GLiKdPXc1Cy/BZjwvVde9Rf7AEz41dh3qoyQeYQOuPzoZuhGAWrCtaxTBwTSyGpnUmk4cbK4BT4K9DKAWfRIFxl41YEWHOVp9MUgGQe0vqOyTTJASOQiCRV5miKZlgvk9LHdHApsEcrJZ/rtlxcIGkDpLSwKjRm9Kk2xnULy/OYdat27VR9uH7O3RnHRmSwlrTeeJ6AYXkHW+1Q6kJzwpLrTTGkgMiJtCV5NtN/gFRnZa7kJM6R7doqeICNnemZPAc2IKYSrpX0rmgNH1O5oEPoS2FQ9Bm7oR44obzQO9jNKF/KD2P5f/rl1HQryLOQ6kbaDpQd9aRsN/KWlXxsQZldO8vnDXfH7KRNQZzv//ahl9qDnBQUWwz2FDrFbkaJoc+ovuCRaZdBN8Yvk1or+8vBoz3os3PcZZj2uBhhhFciGufHG9Euwhu/vZVloB6UnDjulemVq7+qRLRCTW/keWHbQdl3vt7/BiMY42SOB/N8Z+JaLCv0g/K1KJr8WEOfAJtdv7Ahu9X5+1OhQHMNljDmszO+TfhnVTaWDxLGcduZXuil6CHPkF43jnsFfzKvSUriK+VLQbD1eCVdjl1TzgabxLzUC94GMxLvv8twHDh5MyV0XRKY1/2gHJnXv270c/D+FG50drlVYgzabDgO6U7KkA+XuLwk/GqM852wTUM6NIzq01Wybhs+M3gWeb24HrWAsusl6pfj3aVFXV2qjwv3iT8qXEb8EgoXPq61I4XtrZFvO1suWEza9SuWN/L+LxLPt4HHhfuD9pTfMkaQQtEbZoY9CxwaHpVN0RJzZKVXp6RyUzZRVe5XUQrbXw6Coik3hk9JJcdD1kCLqZVRLpxcSmN/v37sjr0YcRhsB5TK87elA/4zL+en+zulvODAC6FBrLH3Yry4AlijT9HRDGAyphAN7XFpP7wCZRaBR6ygLKizuU5S2eaF7g/3VabtpFzUKyDXghFkUzBMsjVNHSrDg8lcNPmUYo3eJR9ME52wlpeMWeiKnGUliuRiA8WyIi085eeuOG0KIhL2UfI9RRLqyrIwZhtEguRDptpwlfpN/aA8pZM5JWt0S0QuC2QXVIlCJXlMGjJYJIq5qAlZPtrF/Nc39ukPLOiAafpntn72XDLW1+CmVsqWqdtGDMPkFoAwfOhQjtBqJNZa6FPx4yuY+I8/scDi8WMIRFrfln9uC8TVCexHKK+W+utrjAFzMvpFTMcfoT35ZVgZRL4EHB8Gz0uBTFvFUCJ38qwDIBybIZFMDHxkvZyxlGpF6xD5dLUkXAZFctKzbov5E7aUFYv0oGmabvSUNezjYsk9A3N9tqbCitGH4gjPrhe+ZKxB6oE91Re3Z+Djf/3+/cOXs94AEhGHXBL7FHMzVYiqvI8V/vq9/4pdwJvhr45/DfrVmN2J+Y1iwfwiHSdXpRN0nC93Ue286oEb5+/KN4kd987ZHhz1lZu/EjxIqTPjrsV9D9IQLOqyTkPrnfghtDEtuHPiDLFPSykqfVsjzgBP1L+oh7b2O96nFud90hoaGKxfZk7GdhRqNmqe9Da4rsZsD6xd+DDc4cEJFuT+oWGJPXTKRuHtEXIqfHwnhvI8cy2kLwezO49nZz8H2zmZXolSSLeDnBKvO1iZcNMw65qKT5SwSJiNlLJu+0v4y7olSqnkvJG3IEWIwXRUju2A1khFqzIKeLTF+C1SWmUprHJ20drnB6QuENvsKmO7emLqIVRpSUpcQ/1YpAS1StNbQ1av58T9YtqD8e3EvzXmaeBGvQ0RKf5lW/tSh8iEV+gaUF4rf7SfOyllfHxIJ301IsmjObPUYJ6n0JxflGRXS5V0rwec6ktz8h+QL+VVOdEacT2pr4V8bHBWIjLck3JYts4Ioww5hozKZMcj8fj1gcfEjkGqmXLspG+d9D8b85GJlvBTRP8/9XLKpb2i2TBsLlZMKkRbN4CJZkafpK4bUPsN3YDJNJH7dF2QjChjyfw+A1CXdC1UKswVMRgzmDi9d+xx4t6ByTA51H00xgiFziZ9rUzCUpX1CelzA2Oeg0+9ODGkXioJoiIywOTxcckN8T5ECVzj7kia7klvIW5SLkp1ltysUMuBocHBtD90eVqSZ0hFftEI5Ye0Ex+dmRQua2MsIflgTLVfn5Pe7VP8nbMkcX0JNSIt+RoiJaakHXgK6UWzYg9yTpSizi8B5VDA0XFoqLLdJfgYK97PZ5cSLNmC2EsBEzE0xSSWJE23i30iANNayLuLeBGSlMPiNrGkcHP1vrHyA2xVNbYqhlzWakvPhcfn86Gf0Uj4UEUy2xT9P6XlSPqMo8iiPqzdrrhBMlmXvWie0m1FXEp++bn/Ureq/vdzDWbrc/hRoZgGPqkEaatL5bB4SiHJnq0K0HyJSfrUUPK94Vdo1xp/8uWMDyk40n1TgPAZ2Jcd2w/G8xt+XvSie9yoJBI1Q/FKPauI7PtyZSS431C2xXHSenCeQWyJyJmYh2ROZ9Jt27vG1xTGe+Px2yV5X0pcLxekYLTvjDZ5vruIZ1vhyDe2uqm0MeeB1jjjlw9SDrabltXUiR03wiqXB/64+PXXvxMn8CZlk+VPS1am/pQgFyIqWGbfuxr9sbGlm9YYbTCn02K9aH4RZIIqdGOtEM7s7zzbN+ZssE8sCvGeGC4V0jWCNqTzJCW2u25Gt4M5gvZ9MlomWZWgvLiwlCBI9gbpK9gsZD/YtszxIniXGRxfKsdt5y9/PThuRo/K998a5y+NeT4Z5xMhCRJpv4MFM54iYrRGFMM25aPgQYqq0jsXDQXPJsDbDC05rZLzUFbpU55Kc7kjqxXSVhgvEFUT3W2/6eGuevnddw2JcibFgUXi/PhNPerzJMj0867AoX1X4FBJmmgnI2rFUqJY0T78S+Eazvxoa2XimiKv1gvU62KBlU2hSIeCtj4PxpQkeqEMvBa8d7x3iRDM2OqGz5AcFf1ex8MZ753+9mCeCkiyP+tKyYeIa+We5DogFstW3wDJNFUkEWfRCV41DWQ628um8skUqXDfIXJwpsn0RpwnrScsO7bI7OOzT/OLGYPJYo86Wnx/ps6G004X9WA6qaKovP3gqAfuH0zvtK5rqC/NV1hFFtjBGGkJl2WGvpp6llKMJQRbd81iDS2VUJvO2zi5mnO9OzWtfdrMTDc5LcywnOUx/OTlLumb1g9VVV+dYPlHZId02FPSMRcxfpwDsj6zOVx+R2LtG1lrgcUPD9nuSl5a5cnq1ZPKZESM97wCcAOGJUkEe9MEditY2qWDLgqNirl+L589ZTbdJIR4PJaI4lgLaavNiSoHTnQd33iIOTsGo50wu1K+5gZWKLtunrLvy3ImonpiX+nT6OvPCbEBmXro7y5ll+zwVn/Q9XSxL6uaod5wBKk5xTJxHJo8+1zVW6GUDWziXGvqrFCnlKT0woXnMYf5EOza1hQ6bWUphGDPWT/7eyyVnVO2He6Q007cRXTkf/1u/uOXs7xmUknUL0n9pANFOzNWk1xebhiZeRo5TfImOxHT2W+Z2887X61STbmUcw1P+nxjPt653BghnyEp061rfTNVs4+YPz5k+1HeaBBxPVx7xzkpKbHdDu6vL7wcd2ZrzDE5vzewoFeJqyO/kGhkv2hDa4RrOHNOrtPJ4eQd6T/nFHbRsko0d+Y5mDgf7Z3RgvYI5q1Tj0GfX3A2oubleRXYOtxZmGjR5oqJNJcyZdMutS1/YjHwxdYJV9ZK/xCtAH8ozuKs1COoL3LsW1plrUmOZJbZaiV5UNIkJ62/mJVwY55LMtmdMaFZFqakP/EsOVq+vSpI2BYwbU1vfa06KHo5LTn5rlWNxxB58B2hRGow3kKkw5XX059DJoqPd7wN4hps90zdN+pNopOy3fR7npeGd7kSaRBpMPtTyFC/YRjb6y4qfsnUvbC9VPknE8yPpbFdsIEYstKlE2oqpJdKG0l2uktNd9m/YGn8kKaGm6iSyWS0jVDOTQ/G96b24eakJc7INSQSImv3/j7X1NtJRybfNoofigqxJ/EPIEL/8OU8fjo0W7UkmdPBj33bsWUoGzm/Eg5nPFSvxyTVQi2Z233n9X7wl/rKlguxw/m8GP/1F+bbhT9OZi5KLJuX+pQUmE9Z0JLCeuZYSFMXLMSypoPlvmPTKTEpX17JX/6CmVAl2TTEetluYgJF08lbT8yUOsZjapEd2sPtx6EzZ/n+5seQHNBgHNoFjuc7Pibt/U0T2LlYM2OpeNKkHpum26Vq4jwh2RAONGfMxb+1ZGz7LoFAyerBp16YgUFNuk5nkRhi6mWNdV1my4RfuA/aOcBEUjCSXsYSzIoS0zxhm+qAk4vrbHz8v8HjefHvv/7O9e2D8/d38s830l2SOMMZ/ZL6x+5YmpDV68UIyutOOhLb//lCmPP87YNxOeM7pDJJZdJ+78wTtkNTfH80vHfmozHPzvy4KMisnW43km1/bAfqvmyEf3TwVqUguq+4+OO2xAdZlIe06yZPybBr7aZTJjzoozMIrstJ251U7xQP8ZB5AI2Ibe1cP2MShVrxqb2uZchlw8rE5oWn9etZUZUpibyQLIsBvKuDLQXZIs2xbvhnBcif1NbW+6beoS9v5aYVA+7qJ5EtR73MB58foVijiW0rHHXj5fbCVir9ZVmrPrpKgjbwigDD+B8n/+cAJGWs7AQStyshOYgkikDeCymg4uTbTaf9NYk+ydvqDQtkk0d0ps7MT5USBvFcg5uwVWlu6nX2LHG2r2hBZKbAnegnsw3Ob891c6AhTQS5NnIxycisYJ/TQsBikrxrqhdFZRdG2XYNrJLMu9EmczFwND0MOZI9aZ/7WUWYXsBwx70zzy5db9lWSfg5fNNQzCKxVcMyXKMxW3D9Pjk/Tn7/++/K/zyf7C8bZlUIjRAeNQFb3sAcTwNQYlWqlXwvlH/ecQbz2fR7eiaJ0PNkvDXmc5JG0XDw6vgY+Cmt6nhvxL1ht0riVbDslelZasJd03hbTxdFE5qc5KTZD2FGPO1QJlY6uWpfGXkNzlJe+ZwTt0kfg7rdxEweG2aTybflfrlg6X/FMC7MOXGXaAGzZRxfRm1gLpx4II14Xm2NeFKKaMpV6xnZX4Po8WNI9qdezvFNe6Cch17ElPDnKX/cDcmwpsQD+1eVOnbLWFQ84Lf/9p3v/+WDt/lgz5Wf/4+NPhrWu1QYX2+Ly5FpLYHDsS0pV9uZnpinaPAbg1IKAq6rd7JdfW4ukPeNNCbvb7/w7f1NjCEzvt5/Vp+3Sz30+P0pxcgYtG6MaYQtkjq6fWYTySAfTt3XjZM1Yr+uLuNzKtheyK87uQpWrXyNROs7NoyUHlp6b5kUhTI3Tft6sFepfsLVR+c+Vixdxy894PkIrEy2skM2OoMSwb5kZjktfSaZZ5NVqmUNZ9xkQjZzDfWqhBjZ4P2//8r5cfHt7w9Ga7T2If3uPvE61Hr0k0A9tpukfSw96nbbsH2DFxH2Hv/1g3F2zv/ixJtjRQLzaC6vZ/2UwblSqj049hu23eDrTxw/bWz3yvaaKFuQ8ySVoH4pREvkt9CterySptCdY1Vp4U8xZSP/mCancpDtRvpJUQoj3fDmHK2QNihfoKYq1eFLQpaLu9IIqtqPiIzFxMeTVEN0ju0LRKYM6Y/Tl4JHofj+Iwi3ZCfhjOfE3Dn2oj6+TvrbRf/2JOYTgHyr2hj8mZcz2iozl0wLU8nJ7PpPVwlKoFVDSeqnBsR0zvcTLuCCPRf2bWcy8K6ylBXRZilp7wR6kUB/XyRiLiOvaRWTkmhn/umHSwnqEkrOydWetOd32qU+71ZfSVXUwDkn/eyKtR9D2SukNaFDhAJT+WXo3Eg72AEK4BTLZ0YQazrJvm7bvCRhBD71C89Lw5qTQRQNvdbiPWfJ2PCkgdECKsdK1oqJckg/N02gvwfUvS4/o4YyGrjoschLZKHfm8FaLdj6FQb92eRlvT6YUxtnCiunOISW9LEEBiF4VzLSlkmvmXJLpN3oSeVe+7UzHl0YmhbktDi2I7SCy7bcR2ipn0x715zIuVDvmXwkyrYiFUyOkbzrswkLSimSV/oN8yqBR2jXbL5CnmAJAlRi5hWI62kHJqUsmeaeKUAJiE0onFQqMBWXscT3sjMpNS5l7YvxuhK3TcKCyJhXXRS1iB3sn78NCUbCHE9LWvlcUDALOMoCgf+Jl7NsG8lcp0QYMbImf0dhHImZYf/2pqXtAd5gPtCwJToxpb/o9Ts5gsd/1Ys8PTFrJe87271SjkKLNQDynSAxhixoljppG5R9MGaiTeP8RZPGcoN8bOTjrxINPL7x/vs3Ht9+p30otTnqgdVMmyfzvBjf3pg5M3OipsXJWhzRMSaWjLKH0r/yrnWK/ZEhEndTZsdeVHZ3UdouptKXk+H9IsJJcQocdtMhUrIxs/a+X46DnDPnJfzI+XxXHikZz4P0cq2pL0T/pt4/C96o/GRnDF/74AmhzJAxtX/M6QlZDokxKyOC8xfF1//rf/lXhnfSPyeZqtsdNohdh4jHgPOBYMqb9nh/vVH+5eD2f70wH44/nbf/+39y/es7z//vV8KD7a+vMqd/2fBvJ/PtJH0GHne1IMfXjbxX9p//Qi2VvVZ+GMXuhxb2KS3EyqYJcB3sFmw+8PnEo8HtYFoh9gPvDX986IAbm4RTl3N8OaSMouDFmD8b9cjcf7ppoltgemUOuNWBW6fsU319My03k1HKRi5ZQDoSV5cCrXXFW5R7hSQoGu0J06lFHoqJr1awSoFWTeKIlDAOHSR/5uW0pIXrnPwR6pONkhdO0AyjAS7HtwfRXJjMtG6hbEQGj+A6lyRq3cI5GSUlaipEyUwLWujk5nPflEK9U1r6z5CY24dDk0igtyESwdUZvemfmxbJ52jgidGbetzP4cK6TfRnnbqWYXkeP/vf0K5lrXPW8j2ZNNqg79NW//AfvqbE3AmmFtCeNQzwFU3w4wZM+pxF85NJmOWWUIjQ5wmuRlnCd0gkUiSGd7QJkKtHQUN/xNBZUrSiN6c/nvTHQoRmKEdZ/awiJD7FlWvxsT6TzwoFQMKRcXXt63570H59Mj86n4FWhtoT3ZyTtLHcHxIZlCORd+1n61bY9rpIfIHtu8JulzRUQxNTH5/VYvnqx/EVBJUqnj9zPh2zyRjCVqr9Ser3cTwv6Z6nzy2QApRHkH5kFq7nk7R+D5qE55RVoBH0T0/sohyEr6qH+UPGp4FfoqKfI3mGvRK3DVB8ZEr/4XP+T7+c+WJ2Rf+VEuy7s99g2w36CzErcXwI8bBULula5ILDIDaMyl4VYd6ar8nftWRUxnEU9m1j2xJjTub7OzEGJU6dRuVGpI0eheEnY3a6DSaT69lJfdBDEkLOxmgK3y0/H0ROvD3fFS/39HXr39ZuMMDKslINjeW3Vx0atUNPjFPg4ijx4yWs96peTPQn6INyS2w308I9CjPftbL4xCs+nMjOLJ25GVENa07Kin/3DeJVBIH54eRUyaWQ6hcsVbw/9HcNJ+egHpDYyOx89EmPQT0aec+8/vUFK4WR7vgszFlpv108vzW+//p32vUNvn4h7ZXt602hsk3so9kmta4JZd30wvgOZtpJ/9tJ//bG+fffuX595+P/eWe8DcrrjXLs7K/q2/rvF/524o8nqdyp28Z2/0ouhfxFD2RyzXaOA3KVdprjJsWTD7xPzm8XyYL6ouGYZ1OmS5skH1iu3F/+SpQbvu1QTqgP3n4ZXG+NcyjFriZdLsSOX5nejE+TeOofMDvx+MBxZj3QcCeTcybtQa7rWc2idJypqkJKl+YX7x06RFc/mzbjtn/FUqbap3rXuF4K50+V9j4Z18RcIVl/6uWM7uK/NAeUPWKmPnGvNxIVb5M+J4+PtmRe65uJH0eQDt415QpXKTaXtGn4JM3G2m3jbiukV+6PkpOi4IfjrauEiU8Dc4Ga8bxO6yTMCDlRNtWsc4i4LpG2AndtQaHmVKn66bYyBaPgrt6MJCFCfHqHkrHddilJ7GJ2iftzLtRc8Vn0vf8I6pWyIOGQ1AZ4aDF/xmSYMfadEaGleujfTWtNIohUrLg83SZWjFLWyU7SuiUStiWhXawQM8l07a41z9UYvUkFkw7sZVcf7opet6Rdmy3SRHxeGSx/5pjM9xN3lf7j46Q/xzJVZ8rLyxIOFKScD8yyFDu1ULZM2VT15SWWMCQtZMn2cvqjMpBHMi1rX6y9ZSaZcKrJJpHVbn1KBPPahcpuGD8GUh4J32UCCB9EKur/XGR2hiIApeT6HAbJ1BBTU9WB8oBYsYrtktwzlFSk6iMkdvkk1i/hK9mKXD1FVr+UDvK6YHw6/FlXSjwdemDPTmajHJlkNygHX376mdtW6T1xnZ2P84OWjJ6bnqRpIhiYtsBmsG0ZH5nzEs5/FCPGYEbjyBuGMTwzXLHcJSdutdCa0/rAHw96exKhBX66Vaxm4lYhzWW+U1z7Vl3gqXgwUnC9C/PpPcu9jjOaAGH3LxpSWM0acDwRmqROlbAY01US3X7+Qk5Qksh97+NiywdHudM8MQJivsMcGNdaCUHUzNxt+U0Hv7d3Ka5e/gap4rxg0cj2pNhOSQc5O6SG7/IKWkrkbGyHMSOvXsbU177cSKUwYsOb8/zlISKcDc73yfV07OVGrTv2ugPOfH4Io5GHzOEm3GSQ5YsERTleF9fHr7Rvjce/ndixY9smpk8t3P7yz6S9Kl9VTJhFe6hsd1UVZYeUfSmZEpbr8qBW9dPwQ4UVRVrcsmvoyOZY3Mlxp74os/T07wu61klWyNvOXOAtZmBz4u+fGmNpsufoWBVv2Z8Xfl6EVUG7OAhjpYmbDq6VBTOqEyX4mBI1zO9iJtl2ARv4huVLobpFmFNfrWDJhVrgeIG57fTDeNpJ2zrX+GNV959/OU3cmHzbKfdKfalkm6TzAeXAm9POk/O8eH48mNPVWySoLMmdQbGdnLTn6mkutidEN4F8hzPWg9aSMVURAAAgAElEQVSenTDj9lIo2RQjOCZ96R+LbTBWlHdeJ213MgjTnyrYRu5P8GDbdrIF9jJ0c1rVAjkNbJc/r34RUzWeScKC0BA4lbXANk3cLCf2JCmi1Y3RAW+6UeYgQvriMuTKkPHXNJVOheQw1iUcSS6Z6Jq6ltJJNUi3+qM8zrVjeQ2JlpF3hPP+dHx2HfxxLXuT+sGP9w/Gs/H4979DrcTtTp8Dt7WfxIlloxIyctnhlnZ9wVoVUrv2t+EGc5MK5+eNfDtI24bVho3AGHBN+sdTCpqsx8rSWnOtoYqlTIyk2zZXMhsW+7rVFkKUqf9Np7039XzdwE6FHKcBTMalSt/7A7NELpXhF70H/ZoMHzA/CDJl7nrUnYUDHaqoplRD5k7s6vWnaZCYCss2p5Qz75O4DEbg5yeHVwnXpVRY7OTRLkZzCg88NTJPSJUSNyKQISPnpSMWy/bPvZw45Ey57ZRbpb5sWLtIVyPSHc9B+3hynRfPjw9NJI+DbE6NFQxqkFGWiMgCjq/Ihv+fs7fbkeRIsjQ/EVU1c4+ITBarp2fnYt//qfZmgV30THexyMyMcDczVRXZi6OR3JvpwZBAAQWQTEa4m6nKzznfmWvlMi05LyOHcR0D35x222gOziQ86JlYrUq8thU7KBMe2adE7ZuYreYb83qSIykvN6KAv2rgEtGQfaZAvRPeaF+UgxLvAnXNVDnpTYqotErYwN3YzGjusDX6qUFYhDTBlhUjKVMIxbkVqAW2XQM1eYqZiUpAwLtYRcWTejP2r41xVuZVFO5boWisuBbywXl1ZWSeHb+xVDOSCT7eH1zvHzz++d+x+1fM70QM0i6csV5OERT7KV1oKaunqMC1YtbtU3xhiu8bO15h+xXq/U7ZNmiyaWV2sgfjj+94a2xfvrDUIVpjG+vlrIxLN2crG5UdzxufSd6zS+d6LmBa/xC1og4d+KUENJWP45RdMY6HhAF7FRD8XC6lWLpdZKpQ+vWqQm0QPZhTUkYC/O7QYDBwk4FDCNdkHBejd/LDRLwYB1aTuhe83KitkUXywus5mb2T8UHUQtmAemPEBgkWVc9Uc1U9nxys/92X0xCXpt5ulE3lzojJHAfPf/47TKd/u+hXZ348VGa+SqQ8iyuuzYJoQ4OxHw+uPjjfF1/1pdJ7MkZgjxMbyW1TvuI8G0cmV++cZ5CHsIPuG7127Rr7XNm0YuEOjFoP3A9mPwDY41U4kn1oSJOQLsmgAnscvzQBHP0hzMjXtjaGig3PYmyWFEs4n0Sp+NywGNQBcSTnTMzescVrTZJpU0L6488hYJydOYKtqZrYb7tURq64w/6h6Wzxi3a/CyVy6vS+xocE8NWJrTBv+ikT5/mAzMkxTknvvryQtZKcZBzkPOnH5+5aooufk+VFabcqIUMMY84V8jpOqafuG1kr1A1GkE9Z6+bZGR9PmInPQSmF1hyqTBNbEZ93f1Nobl6XfLEvG3Vz2j4ZcXGOzvmhSbtCeZJyG1qDRCNqYbal342ghzHN4HXTrMOVShZxrtWbwalqYo4LXOuz4kmxSdt2btvOaA/JH0G/N3eyBLYpo7VYwdsLLRLbDpgT99dVFjRKubE1mLYRVtiiEHWw0OBcz8J5Gh8f70oKd8UWSnBW/jrx3VE5phAfjY1HBGMMrqMzL8gfqSv/uqD5Wn3IpOwWYJJMkabk5nMyjli/uGs0PhK/Jj6DuqtU/XSb5DXlg5tTzbQVRtG4mlO3wOy6CeaEaINaVBphxp4FM6fVopY0tRD+CY0GbIRi26IvO9SOheHT9HsXfVDFIEdfA6NthSol0RVPbvVQ75sN0NqnEIIFfUoGpwZiVhrujdYKWHIlkMY4obRQvGBrlG2njgHhuB8EKrnTl61pFFEfrs6MyZidILBdCJFkxdFlFxnAHFDvRy16MV2pWX+KTeznGsg4VZlvDWqDdmOOQzf3MRnHYHwXlnK7lXUTo5dz0xCnuOLqZdoWVKBurpK3BD2GEKVjiD21hXI+W66BlAYs4baqrfzpdWUrYGJdpQfQtZ4shbgkToiFG9CqKEmmSvRyg9KlXLoQAJuVTO5qY9yLfpc0nC45aytAIaYO2FJhmX2pZSesUf1c5ozCTBjPg9JE1Pei3j6WPfQvvZy3rxKCV5dk7fitM66L0R3/UvAvRmlB9Ik9J3Yv+D6IKIzhZNdpWuNJmnPMB31APyr7XriVjbFNpie7JyWM+vaFAN5/f1Is2UpQHPzmMIM5HK8vQFL5AZ6ivB0n8ceD/FoZL2rw3SuDDr7CTyO4llk4UvEJ2YOt+bIqLZ5uTx0mqQ++3mAbX7Bw3r+fEE/8/E6/OqMfnD25BnC7sBq0uAOFPFbWx60yqfKmzlPa0lvVamB7kARnD8os1NEgpVv2nBQb9HHS+6Bfq8yKYIxO7yf98U23F8qumc1W6tauG8VOlYLLKeEmIT8OpX0moDVynsSlly5nUtrfJcT49VVm7dtGhDNG4fr4zvMfv3N8/4N5DUr7Qm0br//yde0x12GULCkh2NRe9+V1x0the9Ew8Hp2+vNkXINtk1fYdyNtEvNDTTqCTOc5VtuRNJMZXP245hZWnPrlRuRORsXsB7hu80jn+uFY7XC/8F9h+2XD+i/KOfn33/B5UtHMY6Z64lp30dIyub9KSGD3F5LG7HeYWiHmpWrN+5p6vy4FSTFydMb5zvGYjDG4v1a2XUG8/9lf//nNWVyUsUVlj5jCU4ZTtkbZnTL1ck6UB2JF0y6hK1fTPnSqBbl0mqsPWVS5XHwid6BogjrPTjoqT/xT+RUQg0Rwrc8VjZdFQNOmSgVFSqSfphChz3j0OcZiAi32zQxsmV7l6Eep2ZLfr5cEXPkSzDg1ROinyHae+q/GcuFPrW4MRTjkp4gCrYjycwWytk0zta6JCN2OP4OgYI4BHc7nk35N5rhWSnZnjM7VdVhGH4yyPssUb8gpy46UElnogllfrGsK7XLr54Jg5ZiiCSTL2F6piyRYW2V0rRPimsxz4UxSN3y97dSXG15TckC0zghTfuUccgJ50cRZajaR8XLKjfNJKrAqud+cnyuX9XOHEJ1zrkCQJXLRlHcJDGqBKRWVlwL8Gf33SSvQcyFav0URWHvJNgtScEWIuKDDLNYlJe0stpGm+UYiKagRAnx9ijhsBWYVNGTISXTtN2eDafFpE/lrL2fJ9QJsQimaVfxppAd/+/Ur97cd+5qMM/j9t4PwxErKwdKSHBs5lE2SaUS5U/fgy986+yvcb5XnQ+zYXCiMuC5GH/Tv37CtMsqrkJNzLiE3bFVlsVlQirP7jfEVcp8Ks7FBGQdGhXYjHM5TA4bnb7+r3NoUqVD2Hb78Itva+8TmRZ0/1oTWsXOjzIbd79oT5gfRjTmMrA1/uVGOTj06Xhpm0GLD0hgl8N3wl1S61ZWUNrRXdA3Frh8LeLUe5KsEg4KH8fztDzI7//i/fyfG5Par0Xvw8WPQx6T3gd9FWHDtzhlrHGxT02AvWgdhYMfUg/L1BUplUsjnIH574nZSfLC93Cn7Tn39iteNvUyKJ5sHj37x+H7SP57050W9veKt8fXv/41229h+acScXMcJdMxORlc838wHrRW+vv6dar4EiCkBu8kcXW+SRc5wgagPX7fsrr1jGMdv3zg+TpyOV2f7P75o/8kd8w71xOcSwN807IkFjPb6JFvhKi/YWYhvndJFdi9N3uW0m/amMyhZdYkUozoUXmAY833H3bm/QY/gGQMw3BtRdAJabhQz2l3rtP4Ez4KPQjwG/RochzJO/9LL6bFGG0OiXbdKlg6bAnRVLq/bxlzL9hVWaiRRjKhVjKFEvNviylLZjX03+iGqfy7xgaevPZhWGBGmVKtz4k1f4p9r7PVzmsy2bjtCdk88E7eJh4ho4wjm1YUPauIMlVIptS6xAdjUasGaKGtpSwRAMFP7teRz0SzlgpUinebmWtkY+NRnUkooZ8WTEZ2+oMvGCnDCfsYAat+I+sgwcibX9WT2k+txEhkU2oqIH4uEIAqg78vE7ciQbgYrtjAHul0WY8jkzJalaUztY2df38mNut/wbZebBAkfMoLrvDh/HBx/PBjHBRGUuqkn3itlc8l+Ah2Ky9b3eXGDjMtzOsNUFcyINZhacr2c5IBxqdqaY66K5lOcwJIu5k+Ujy/vqigQrPAo/Z6+fd6YqlJ8wbnBlW3SLzjHAprpkLBWJCDpigqMmYt3PAkTpCzmCTgR288AYXLVWktMsVQtP+1vpRUND6tB6neLldT2l17OOhbH55TypPqObw/ydujEOwZ+NuIycgSUidmlHzKS2JxRdvZ+4DMwa5pYvRTub87XL8780LT26lIC7U1KE7+/MDHG0D8zf3Talzv1tmHsFDMiTgCKB2wN2zbm9Y0cXbxTknpIGN5/fzIGZFa83im3O+1WadVIewoGfCi0qHzdf97kUvt0zvxGDFuLdqO1F6aJ7ue3pLUUnp+UA8BC+THF6J48+5OP7z/Y241WG7UMzGEcotH5NWRNq5OgQxg/vv/G9XxyvOc6BHYmwagDq5XWNtpLpewCf6U+DClb/NA59ZBIIatYPcq+XNEDzwd+dtye7G9/4/b3Xz/zNShPHVZWjXFM3v/tG+//fOf3f/ud4hP3oN1e2d5eaV8UKRiI51N8x7eJ7V3lZhjF7kDlehZmTbDnGobkT0VQPx/ElfQ/lrrJFX5UbJW8xdluJsLALMuitWOof4suiFbdbpR2o7wlZoN5JAwoua1sFod5EefJ+NGJK/AXwzc5X4hJzicRKX7yfJBxYfVNh1t+J6JwXn8TVT+kzS0EUTbCl/Ycw6mUmmz3RWwozvVxMs7BXEzjv/RyxmqPSgXPIEZfe0rj+DEwJttYjB+7hAtsEhLbTJHZNYYQaa0oKKfuKjdnNq1Bnqfqb3d5DxeF/XNC5+FSctwUSf954sYEVkgr4fg0jA18/RwArtCYbiFK+q3q5iyLK2P56Qwifx54+sA8jHEpe2UWDTmaCUI1MxnTNGBaiPWscsvr0Ai5I1xg43EYOSQ3xDURxGOpHHOtYKScilM3Xn8k41y9UYH+VEYMdUnaihHPTjxOoTAw5VemTNsMw4YeYi9Ft2mBOLpuuCmrVPubs7++cttfST5F/hNmcMXF9fHg/Z//zvPHkgFu6lezD+J5kY8Cm+GvqAwqc93SmzCqvszR5loZWXD21Z+lgcu8PF2UhZGyq9HkF50u21iu/XCpG7UlVn1NSieDQcaUy2NR96ZXMFs0qLW/wKDmmp3kgslN/NJqln6pP4yO+cRcHtSYneYdt0JUTVtBZoPd1GaRDfNGWtH3mGsLMBVaDGu2Uhsed6r5X+85p4iObG8QVzAeJ9OEtX/+cRJH8OqK9MsXVD41X1mUSTOR9WopmAWxLVnedsPKxhU7/QzGx5PYNmiGNbkArO8oNmCI8XA33CqOM/ITi//5WwxsFAqG2Y75hvkAm2QcEjG4VEBb000jbKT8hqKCm76d5RnNKTXJPJLrSFmXPLk17eAulGb3fBo1jZJO3oKoyZxPcXeGLQFDXS9nA0QET0+N/iUo5jPfcib0ZzA+BB0e06QdLsn5cUiw31YqWIH57WB+XMyc6t3e7oAYNxYFi0KxDepOtoOwTnw7ySHkabltbL9WbttXXvevjPNJzE4fF3N0jvGd57dv/PE//h/6Vei9YW3DvTGPjg8jK+RLoX3dNKWdc7lU7ksl5VoZOcyiEOL+HBRkWfNbxVoT6nJMkf4Myq2QuzNqCBwdhnkTgG0Dr0bblt43H1gWypReN6oTRdGCwnoueVYFWGnqkYxLU+Biic+A8wlMMk+VzN6JPLA5KL5RSmM0UevNkurB5hLFZG6k76QVnn1o2DU6OTtzyAPtzfFtx21jK9oz/6WXc5xSJUVbk9Zdt4wP12qjAqg/GwyYyXxOSkjwuzWdmGk7Kl+0MO998vF48sfx4Pn7N86Pd7y94Qbn46ET7tslf+Tr6knVcqhkXviOSgITaoA10o2ybdTasNUjjg8tQFvTJLm0ohd3THpuDKvEpandXuW53GZZJt9gVrAtGSXB9RkEKVuSaa+WDbKsl8ggoxFR9ZmYStXtb439v7zQapH20iZi4rBQJ5WwoI+xUr0C2+TcXBFK9I4sTjOYXFgOxvcn89kFbS6GzdXjHallflnT60jm8wE51NsXAbHqrdG2m6TJ7x+EJRPjGtCP4Pu//+B6PGE2at1p+xf21zvtfqO9GGVz7LUIlYlJGPLjhFvB7k5hU3zfS8Pa0qx2yPcpY3cz+nVqx9wHOYN6l+vDNuFV5ruA45YScCTJVjdKM6pqeby8iIbfnGEX89ShlStHx1BiWVgoTOu4iONSj2pgZdNB8ji1fJyp/Mztrp1vXgQVp1DqvkrxlK/zKtQqPjBLeD+uzhiDfg56H5yHSnRP4U7qDbxW/jIacw79i9mDLEnU1GI6F2S5GYT6lzm1N5nXxMypXthqYatOpwFS9I+Ao0+Oj8H7t5P+/mCeB1vewUIhsOvL42aUu/MZK8Dy/WnmIR8BFlhVbzxp4sJsGx5GzsmIAommcZ962dAgZMRaJl8SXvu9Uor9zJlMM5pM9XqQLIji6+XU0iYZUEAS0SpHQwjsFWt4RA3qa6F9ecGnY9PI66kbc0Xu5VKLCFUSzAyV5mbq3yOJa01XItYEOxnPi3kOirVF2DMNJ7pE31kDsiwt8yVnhi2O6yZxQCk79CCeB3NrTHf6gOsMHv98Ms4TslDbjXp7ZX/5wna/479c+B4aSJW1LprJfF5QKnbTze0I6mzNRYfP1OHRAnxqPZKGHxeWSX17WUqFogrplOIKtHLJTGpxSnE8RRxwl7vF3eA6iX4wPi7RGKpE9n7fyRn0a0r4/vHAqnJSwx3HGKdCdG1CbJXyU14o50pYoXoVHaIM5kx6/wSNC5mZEVpv9cHoEtH0rnlEyYnfDGtGrQuk/VdeTvbGmJMf//Gg0zmt49mxHOTtDUpjYsrfyB/MYYyjYXvQ9oAyGM25vkv5keOiX8m3b8F1nhyPB/k8F/1bg4P+mEQfzMc3LAt+vGB9yh0hdiK1iumyv9y0i73VtU81WYGe75wPncJmTtsKv9TGHErKjgi9VMjm1kz9sN+VXiMbWKFuO23Tfm4cB2MOzqcmeNcRAijnk4gdmzs2F3YiwWpl+/VFh8gMvLTl+XOhBGlST/mTYDLKZOagl2C+aKpXvWoMfyGy+XHCHHhe0NaE+KtcOKW9yYLVtG8teUmfegpCViyx/VU7xJuW6Xd023g/uY6Lx8fF9UMYmOv9yTh0A5kl23/9ipedUit1M2qD2+2FcivUm8JD5vPQLbM16m2j3jeVrVYI32Ra//HQ8PANcCeKVlakY/tyo7RN6pxWKTu0r8gofw0unDlYDWLAlMChjSCbM6vJykbqwIukjFXR+EU/Bx/fLtpIWlYii8QV356YGXWr4vrcq3zoMYXf8Ua+7sSiV0QGwzvzOel/XFyvlXqvzKEJ/DEPPWcjYBolm7AyCuuBNCxewNpfezmtaeR8nYNrdp5xUO2iWMfXlEzg5bWAnS47XwViatmbKP/xmsxxcp3B+UjGdTIvWQvsJwdn7YlnkNEl3Zpa7KtfyD8x/ma0uuFtpVp14T5maDQ++yBm0jZwN9rW6DkZMbV4Z0lLCd2oLsGABPnClRRj5TemqAtTEGt5S9c01+daHBWN9FPkB4rjt01l+KU9WK5BGVN/X9S++ZPPOz9JCQX9rktlE8jJwhD02lL9lrkr/a3suEvcHd6xmMoWxZjBT2pCKctit8kvWkbBM7DRlSM6B1eHMZN+nVo1BFgrlNuGlybQVV3hs7VSqyh1xKdH18SFWjGLgmmv7JRI8lp+z6YUuD9Vx1pzfJZ5wkPZyoA1pkmT6nUREkx3aYYtn2X8XH/lT7LF54RvrcZiEHMs14hENp/T2xyaJXwqGFfYjphPoD14KYpyXFEQnWCeg352meltMi7l5QyXEs1XsSM5y+cfvvq0dH5Cdf93X87bL8b5AY//6JyPk8e3B1sJmgf3Oan7oLWGm9J+I51plWcf9HnQzptiBB4XMzqH/2CEcfkrvjfu+02U8TkoW+A1KK8Fv90I+1U7qb5K1VthtKLJZT0otfD19i+4S4x/9Sf98WCM5QktSuCuOfB03vY3Tgt6b8zcyOi0TWoVlUrw/n5iY9I+Hjr1404vygGN6VhUxo8PAcoWgsPvG37fKC83tgTPZC67WGw3pYqlwTmx48F2l/l4lsbMwvPfL2YMhol+57dBjiBiMp+T7MnxuMSlYVDajW3/r5R7ob6IMu61wFDuRiBfZc1K3gpR2xp0IWpecfrxgDmYx5OwSfUDZ6Pe3hgmKVzUdyKC8varuEquhPJtL7Q32cbqrePe6adLJ3qvRNMuO3xyHQ+R471RQ73cPKR5LvfVUqRhdEpOGFUT63GQ1XBX7OT+9saJXCRhQmfNKTZPhgBto55qOdYAKIB63ygUsJ2IwfP7P+jPxA6ov1T2Lzc9txRyaPXWbrqRZ3bsnHBdzLpDadiQFvk5pQ4bY5WvHpzvJ3zr9P4kYlK+fJWdzAR7qyxzeVOWTmSy8aTwF0kI8byIs/MZB2c1f0KjihUqGm5oCPK5FEZTsJWHUZi61TIYRRYkW6dW9Qok8SklM5HB01mCZsdrod4adW9r7Lz6MPiTWBeTOEX1y8V4ZYUmzamB0pRWStlFKQJ6qevlTEUI5tTwKNYNm30wlvtBqdqaqlpRKA7tc2qo6S/90/C9TsOp/8XMJYubfI61Yg03xjWYczB8SsxvgzwT+7ydu6oGwQrrKis3aivUVjTgqgv8FTA+yQB10QG3bd3OIZYuzuwSOaScAIRJzqYF/Sq51i1YtooS7FKfV7VFzVuaugzmpfrJ1rNgVZ9V9KE1iSnR2XJRJnytUPiUrykVJ3/KV4D150nsokFS750ZuRhQ+qdGVlUghtYvy0RsXmXmpjCGhBTjVBJcsaa8z88cjViSFtMsI83WHlHSwBiavGOn9Ly9K/vkXP3yDGxVNZ/kDetC8ghxY/rezZbKbc0vcqzl2V94Od//r/9gAATUF+f+y0ZrO61svOYLmzX8TQDi57tKgI21F+xynFhOeleJO8qNDKOaHOLbvuElGVO+TQ/xYjKTKx2vlf2+0b7caV9e+PjWuY5JnSc+k/PbHzDFJrri4BwfuG8UbwwvBJUzhyRt7wfFgls9KUwuC/ZdD5mFotWv8QMysB2mTc7z1IubYE2p1eVeKaX8FCpkhg4sC+L5JM7B3F40QLq6poWXWDjeQntXfOE7L473H1KLoBfYx6DshdKc/pHMDs12SnVevmqU37YiPeveqE0HjJm0zI8LKEF7WzSC253r8UE/Oh6vWDb8varEu2uCHvUFIbCSHAWOQhkNz2R789UWaBLaNqeWQkHGhhHB+U0vWf0KxSe1Ds7nxfm4VM6ZsobdUc5mOG1qSZ8krAzOiCCz4PUNa5Xt9oJ54XzCx48nP37/J5k3oFDLAINnXyHGXhTO3BMvNyi65cnk23886c8nz+/fcWvs+xf5LDeBz2aGmE4k0Vgm1BfMhHB5fjvpj3dlawL1FzlKjneFF/eAzYzNGuk3kgnH1Hd9R5uEsq33M9e60Oj9Sf9PJEL/6ct5nmMh6NXnGUV6w2x4rWtoUok0Wtk0piYXemEhEU2SqsikfNLtalKqrXbAKSa6muxmklyZLzpfqRSqHBvzJGb/KS2bawo5cjIWVrIU/Vkr1VHma3OiqIesljI6rD1XkitC/DPVqwmjGDpkWCqWXAegbU2TxFLV5yy0hiXqbQp/4llSo/wYQab64eu8wIzjPBlj/KTY+wrMTXNKKdrHvizKGwoObjetQPQ7oin2Eqp/WvV8rBPbbPWsksD5iiT4UwyvlixzJVr3i9lP5lmJaRgTL9Bed7w4rUppVNauGnPmqSV+f5zaS7ab4GV7oZTKdg8NiIDeLyxjuVhW7xsBY8AwGHX5SqHcJbWbA/GFz4vz2wfnHx+U3fG6yU1jhq1SOIrIGxKSCLmiKzaYcRLZlRjmCg6uLk+u9/gZ5ItJogrrhoP1ByZZguhdn/m46cZfdIzWisryXOQ9ktpW5ManYMEqlFRol1ewkBLsr4oQHs9Lcrt7x6OS40bJjRo75XWj7JXbrZGzEPVGz0GiJXMysN2wqjyaCGPz+lMNIyVJriSsQql6QaYrbcx9UIriBWo06lHZjoGdB/4qwkBHB0I/Ot0Kl99oLSgtGB0inXaTRSnX7dYcrq4vMoZWHc3VF4QVLa5fvzAXya+YcltmQqZjt23RplVGsmiKTgjP6JVwpWtbjD/N4P1gXseCbU1C3/MKvjG8NBmGzWj7xutLw7/umDXG3PXgbENi/1W+ScWzwNFflG5Vzj/dDnLe9DUBrVxDEGh81fhmi4hgzB8Pxo/fGFslaqFaUmrl5e9vKqXNlJfTnByFnM71TPpj8Pj+uySN+St2a1B2ts3ZvxTG0ATz+PjO7AOeS/t7r3Bd0C/l0YybAoEK7F/1j/RvIaDb8zsf//FPHr/9zv3XyvbqOG+Sxp2qembrP9cttRZKbWTvJMGYH0wG5fZGLYXb3ih+o1wv+HGSfSgCo6T6XHeMRfJImdBtn8wuUzljU4laJq0Z5Va1o+0XZcqRc/vbi77TflP1UKrwp2XiqRt9WpfX+a+8nNvrJhXG6xvhTnhhK42tOO1FZVXzKoAyDcconsSu0seLTpG26ebyikTXXik2KFwaJHxSukNBOxiwiT4XfRIpSHP6IKuW8dmDOD40mb0u0o2tQonEe+B99cmxnCtbpZmLfxodv9ZKJbWvnKFckExnXoXZL0Z/st83CRt2ofOjLFG1N2Vx1KoMjphESy3xp8pZeggUn8mYQR/okKhChwrwN9fLPdSbbTtt36QhjtWAX6uCabJGMSszgpj6YtONLVaPtqkXnzDrHA8AACAASURBVGfCKbZQ3RxvRS9HBrMuvfG3g8ikZ4Kf2Eul3d6gbcKnuNO4L+XVIBH+g5UNYpmYOdvrXX3uZlDg6pPiocyQ7tiUSslqwb4K3GVokMomc0OGaW3R4XqqmujfDwnzs2tN18UhKityEINpKofnMSRprC7o9AiOMcgcUDXd95VuPubA9wIvd9qtUGfnej/BggXNxZC1bE4IW3GN5dOloF7dd6duG9vtVdiRUdbNbdy+fMWtkI+QmqhVsKqg33ERc8DWKP/JK/ifv5xvG14r29uLvIeWbG60YtSXStkatVfCJoYAXlaUwFz3Sukdn5O2Sl/J1SpZXyjxZJungm8MYhYyjRLSf9qLfInR5QQJusBgJRkdZk/6tw8Ng5jUvYoh9OmrHIaTSixLlYnV1z7zBIulecxJj1j+yYvMwuxNZd540rxhe8NfxYOVO8UouaunqJV5dfFv9iA8mIdK+ZyDaSnsTAR9GOWm3BKKZI75DJXFTIoXvG20fafuG3kWuUouPTh+L5BO9kbOizH70pE626cToy0Z4DWkfrmg/eK0vdBRIhkFZkzO7w+CQd86tYhcXl9e8faCPSeO02zXztAHYQF5CZiN5GtuTnu56/9XxUD0PvlMa2T8+XJ6ndhduBm9nFrJjLUbVAuQXMck5+D6/oFbUpup+h0Ns7JaIpWD4YeGMqeAWV6r1nEMaZRzYrdVnYwqoczsmiq/3sWVzcKcKyqj2mclS04WdWlhberETMHImOIi6tbYbnf0EBesKWz59vJlpSQ8pHZaeaxpaA6SuSIg/mJZ+9/+z1+kikCQqJgn7bZTbxueSR4X374/iN45r28MkmvoCi+14n1QOp8J9JoO5sStY4jWPosx01Y0gnF/ewVLjkNwaPNC2RvttXL8WKqlVGk8t67bI0IL42bav3plewYRqQquT8Z7Z7pxOpyHBquffef1Q6G0k0nuidVJ9ULbXmkvO75LRWNWdbpHMOZDD8OzqrSMIFow1oArBuSRxDOY71KYCAXrMJwWtqxum9Qle8PLRqk33AbXOSWn60PrhuKUohIpbQpe1nWxTkvhXqa4NDkHng8wiK1wraDgx8d3ZcVw086160Wp9UapG952brev1Nsrc3tIGePv9Dk5Hw9Bz0pIv0xluzXqDm3bNfh6HMRUAvTsSX+Eps0hLbE5tBZQO2N8U2hAMQETW4FLFqz+4yKmysgRyXkk4ylxenZnns7j374BQpykFez1ZaFWgnFeCjk+hG7hUELA9fuxDsGLYZVpk+aayM6niJClGjQjdyOzEBTYArdUoNeskFV73JeNUgpmY7GJbBHik5frwnGiSlZZ9rXLTYNrV0LCJ6z5r7ycX76+EOGc/UbOi3kN2r5Rbjfm48nsneN5rXTgJxNTtobJLGwTiQf2tQ8eIBlf/Bzhh0lxthWBhW8vG5lJf56r6V6NdVsq01yHjSVR5AJhpqajDpSC+Uapy9WPlt5xDsIUtdd7MObyVWYyjoVK3GL1ByvjkQ1fkGq56W3tIAdzdDwrNtU/CO/IkuCxUJbiC8U5yaIb002lVQlbg7A1XLsLq1J8J8eTOboobleHVmU3WjjNIORXDQjT753rd8mw1Sv1NWCSoZkB13nowQ0NSxx5bKvXn0yjWjda3cAOeRhHZ8zOdZzKzK2m2AdnRQ6A3RoRk/E8tEIZum8iU6b0+ESmGmUm1iZzAcCTJl9qMay7zNHnScyxLoRkrBxUN19eV6M/Dw3ZGirDq/pEEQc68+z0U+QIS9nJ+vuF+STqRTkf+Kmb2NN0iGCS+7khV7KvFzGwEngIEO25YaXiTeYMBInR92mKy2ghT/HwpsFahZ9hvFStHC3+FEz8b9+c9//KSOcj24o8f2G6JE8fP/7J+Xjn2WXnaS8KIPJZseHwEVwJWZ1yv1OtsH0/SOAqxkznSCEfLS72r2/st4369yZOzLUwI+5S5vx+ML59I44n9stXbKts5XNQUojh9F7IWZTH+QmZKoWIyfuP/7HQkoP0XclTay12THF8tvsr5WVj//qCT7CxFENncj1/EH3y/b//g5gXXk62lxde/uVXohVmq1zHg/FxUfLC52TG0r9eRnvdaa83yif5/BQsKtigNcr2isUkr4M4P4jzg2SHbeP2pjjzisQShOIdvIY+61z7wQTOFROxb4wzGY8kf3RyBOelIN/pCvG9/3qXT3LbhLh8Pjj9G/06uJ7fdBuWN7In83uSzbFbE3vYn3JGAPb1VzKhX0n2QbnewTfMN2J2Yl5kl8F73GRX4BaUayf6G/OTnvHHBb2Tt2vtS3+RMCUH5ebwr0arLziVsyYzgufDsTLZ44A4qPNBXpXsjncJHRIwD+xWKbXIW3m/kc3px8D6MkxHEn9cKoF/VCp3dhy7L6XRxnItbQB4f4IPrHTiLMyrkncjmnPumibPOCmmHffslX41hSlTpE76qzen546DViBWtWZI2alirDgDl2QqENDI4KfsLtff80/Rghft82w1JEufZrmCd+pa3htaV6yYv9kneepEzDUNY61aKE7ZN/nnuiaUk/jJJJKTH0bMn7K+rKr/PyMj1mwBbzIkl1a0Dh86/fWGKhYvhqxA/pP1aoQtBOPVmdelwUEumd7noMbFOnJb45QVdZjTJUFLFv5/KPdlLkiWa09oSy+uJ1tkAGWG+s++IQNyytcY6/aeUze4xs1r/eCS3Nmm/shKgSGqfPw02GvQYvvrEphoCJS5TAH6UPVDjVgLdsU54irD8bLWcIswYfy8zcVMEn5mejAtyGeXdnibWHU9cyjgA1+73Kkpe6B2aM5UTzx0KGVOxfKxxC4Bc4jQ4MXwJrWZVeXkzOhSqKzP1mZX9RFJKU1lPOqRc/m0qVqfKesyfoomYEUdBozQOkbkvxBSNpAggrIiG4y/7Of89r7KIrtU4gT0PrjGBCp1v7Ptxoxk/D6WImgwCWZR8kTBubkkePnaF128UUJ/P+zzh3TmNM7fHmRAWQdDBJwfD87ff0ARSc+iw5yUkRQv7PsbPTr2fEo47sHtrhVKO5acjhe9REUlllVjR9Oy9vUrUYBfv2C3jbw1Ig/i+YFHoaRSlrNW4r/8DQrs/9Kw7YbtX7h++53zH39wPX9jzIN6/4pR4FEoYexfHduCsA/8IYG+3eQVmw8nHUY+l+D/otgU6cF0IIxjSHx9+0Va1bL2nbWSrlWWXXrQeb4TY/K8ph7kDtvNaXvFjxsRRbHpBew+lrtHa6LItlhHT66PB0Gw3e7Y3qi3v4nla5NSVNbOjvS+qc+0ffkCsWEvELaTbOQx8RFrv6dpaubg6k/i8Z354wejD+acYsRW4+VXid4B9fW5wUquO98/iBPmtpFZsfnEY+A+oTpZX7UTDsh3/bn9HyfmxsuXnfK2U//1bQHh4JqCCNQsP/tKUkaDuDvsFduasklN3qfgQ2fSDIyG5RfYDdtd7wnJfIb0xv0db8bxVuHocAx83ylbo8/CnH/x5ZymtODRn7Iyzck1JJDOlGLQvayl91AzXMvSPP5sDnV6jJDBNZHYPTVV1X5RVPEYThynCOqfJDw3IrWH8rJE1fZ5LqrXE/Vs6pR2VmSbxA1oJ6+AIdMejSV4yPLJrlVPl63iVXEL8fmZ2aoMfka9DS3Lq2BW4/uD/v5kPJ7EVHk0nl3VQDrmZfWtugnngiKryijk1OBqdMnElMitnBeafp/8DBda6j8ruk1KSmea7sxxEl3gqDkX2hyJPGwtv+utkDRqXQzZm7IxPQYDY+BYqPqovRGZtE9qhAttWdGkGTf8BjliCdyXpNEbZneCxqQRc+ihblo/JCI4kqwpvFQz9VOIUviJNhFnaZU1LKmmsj4kl0xXD+iS4pVNFMAI1g17abp6ddITbxLUkKs/Vx2nz3dqS+pbU0xCkQGckiw837rlijSyybJ7SdBuC6I2+9T+OaRpFR95JZjz/5Mspib0//NX83+lrW2N2TvPxz+5xuC4tDcKhqjX3ijcJBio71AN/7KRHca5eDuZXMcTEvrQ+qBkxxk4g5l6uKIX5nDO3w8lY23LkXB36VDLhFuj7FUDDIzhEjvk9ZAyYwtqadrHeqFi9KHJaUyTyXVXQlUWY95FEs/HhuH4TdK4u8Nw5zKXDNAK5wnzCuLjHd+MHsb144P3//dknk/m9WS+QpbK8ZsmzduXF8peqF93hdz2wfSL8INGCM3fdYDNIpxKxMBCtjzbwXaHsknxcybUqZI+jA1jusTex/mkHwePd6l1/K6Xxc0p28RbUL8UrDY2r6LN71/037reeY7kOZVyZhmUKj3x/mUXwcCCirFlJetGeMPrpaHbFK65liaI9N640rmiUOk6cDa5cI7r258OEofck71u1KhkE+qmjar1RpUxOd2XXWnIk2lF1rw5Va5Wp7zubK8vvHz9qpiGYXh9Ms6LsneSwawdqjOutZZjqHSum+x4btSXLxqOcad4J0sn6yTK1KrEKnXbZWo/5GwPpCHOYovB23EfuiRuGjJ4viwUz2oJZmJ2in/1V17Ox/t/MPrJx8eDPoNzTT/dhvyJnsSzawBxBiA9a85BuS5Kk8l2dt0Ocwz1ea4eyVOmVYrrRsk/e1hQv+jmEnm/bHhb07XVA5Ri1OrcN+PsTp8uKVgkMQfTECoig1Im1pz6+vbTpZGrwZ9V/VTxZfsqa5z+Uoku/2DYJGqn7+Lpnj+ejB+D8f6UVW1CdO0hfc0P/O5YDbI/mYdcJvqr6UZwLdFJkwF7yR4BDBnWvRQ+kaDcUP9aCixmTs6pnjB1w1JlON63TdPf2vDdKDtQbnq4pr74vSFQd++0ov9yqRWzBZkOmZrNjeYCXbfFdM1p1K0JaDZWvmkrFDda2TmGylkwLJz9VsEmVmB0h3GHJpHlRqNmIVeq2Cc3uKzqwsqm6WkaQV0rNimcyosUPa1VrTIWHxcH35dNbntTjzvFv1rD9fWzDXwmzEPVW38V37csy+hEE9U0kQ0/zQGmZyeX5DI+JztN24q6nlKLxDOxOPR+rJbKyorE/KvT2h/f/o0xJu/vH/SAC2PzTvVBySZ94HmQM/HDpIDIhvdOPY6FWayy1qQc4YYrbyONElW9VLUVBDTQZsKWx9KE8t8bW4PMTxq6vgArxtaMl5vKi+dl2CVlTnTdMHNKBVTrwG93ypev62GfhHfZi1qDRUN3Nx0Yu4Zg8S4W7CyDaZ3j5WLOZHwDvl/w48G0wvQCXSf+5ovx81Z0UDzfGY+gf5uUpTj6XAdNP2BCGZsGLT/Fd3ohSqkaWjjYC+o5c9eDNk5FLAyINMJd5V1x9v1GrZtO+ZeK72WZ0Y02Lhpw21L7v/NiYx2EtxteN5IfzJHrcBC+xZZ7KA+DDttb0w3bdSDddmlW93qjHAechwgDCa+vah2swnU487hhHnib7F5pruyX9CT/+GBFseFWqXVnZGFE5Wd8gXel2d1u4sMWk5LpGgKZeeC3HbJQ/W9CXH5MxhzEPFZbYfiUZTH7g6Qwz4lXtRQBWn+gfXjBV9bEUil80h90REk4tO14g20uj2lXYpvbybSdqDdKc2iugOm/Oq09rTNNYT640ZAo2z3lgUsFFkkAHGQ/6P84uFO4t41y+4q93BjzCdap4wEpAyqJXrFpcDmxhAm3vUI6z5DGdp6fMXSxepVkIsJ2b2LdPnujj4lP5ZiMaeorkQojE+2l6kbD9VKWwfXxZFydeC7lCTv9KWx/70+O80OQrZ7IVB3Ai9waOTUE+Neicn2u/Z8Fte1KYj77gkgNrVV8KNuh7cS81hRz0RpMWt051XdlgaxF+9F7000/0bT6fIr72jvDUtUKhpfK7eVGrY37698pu9FelsIlK3YeZJ8rGwZ4nnq0yi5Ddal4vWHe2MsXZnZmhl68umHWwHaiHgQX9V7w5thT08vog1kro+zq8UpSXSL7v/39ha0V/vZ45byC32+TPgd9XniajANVYvIzLkho5VW3f9k4LbhIOsY0VBG4sd+aTHiJcnBiklaApkN1Bjl/qMfMTQljNhTvMJYcryb2IndL8NQQrgvGXf5/0fBpuhTmY72Jp0z57rYo9kZmQ6G5h4b5tzsZg9mXST4mbor/mGfhf17U/i9ezs7QVGs1/M3LguQaYyznx+eo34UTHD9ObH9lu79Rtju2v3JUI6Ljm6asjM+IBC1lLU2p1D5ptWls3zcND8ZKsk6F0uQaJKQFvcq7eY2iviYHGa7YOkQEbYsfirs8fmj1kWXSr4v+uLBz4hSiaSXUu3P1k/N40K9gDCgN3ahNdAPPQ0DnrZCHEadRUyWM+yr5xymz9ZD2VsAtEzd2XuRcQ3iDalp8y5GzyurPtcTCd+QzoQ94PrROuILR1K9mKZgV6ma0trPd3ii3oL5ezKsxr6LP/Rrq11h/lq2ft2oqabaBFWq5YVmI8aBYqsS2hnFjlIuwwDfJ71YKE3GJajGzEsiNYcUpHry87dy3Ha+N4wqiPOmjc45Ta7dApWpOUQoTbm0XJT0rGcpOnct7WhbHtm4itEt/2YlxCsWRRY6WOZnnUw900TQ1UwwpxsTqUmPsVZI9ThndZ+JVieWGDo80+XXz1KDRuvTDQscWldVLDlhcocnbbWMOJ+LCpq3h5cqqveyvQ6Vjqsz75W+NVnbu9ZU+L0Z03T6WzPEqLdzxG50CdYeXr/jX/8Ivtxf2uhH+wdMm2ZwoycgJFGYUfA4sL8a9EKXycel26NeJlcFsT66jc52D231ZqXzTC3udzJGc6cw86fnk6nA9Yb9pfeN1TYw7pE+6HRzjydmf9HESOcAPCDj+eJfwPRujX0rRXs6T2PQlClgFm6E3tt2ptcO9k1NujXGt3egp9msc6kHL3ZWnuSfjQ0OB1vSl+hZwaZ1km+Gvjreh5fl8gXDmo6t8bSq9sxrWklYCqJi7dLl1oxXH+sD+OKklqV6ZmyIfMvXvzpumxfMMgbeeRt4vaLrxaimk33VbzEGtzst+0W8bg0YPox8JTzRJteTqTx79N/r1Qb8+yJuCdsfRmFlpM9lIXltlazdu+S88onPGoOeTGWPltBhfv+yMnhw/gjEL/XRaTeWJmoBtn6Zww4gMIgrXh6bWGR3PQZ5d6w3XXjRXmwUdD18keB3o5/lUdsxx4a93qjtrUc44nQxnry9rZ3uJnvi5k4/GVkN4G6+YF1p5pdqF50GfBsMhO/M8od/xv5rPmegk2PaNW915bTtHT66hPWZaMKYCWyZrU75G0L5t3FrjpRaaG9eiXefyWElfsMTTS5iQ5vSIVcJemHXMLjn5R6ydvElik4oF/8k6XXCKGZ9+UgGF8U9PpiRuM0Nxc3NlfBoqWTOZ1yXgE4tHNGOV4FJBaU6l39OqjsysG7rz+s+lctq6uee67ad+bv+MSEQ4fsZk++zlLPmcRFgxvPqydq0SdP1f/dvrZ/C1WC9ak5sXtk2GbLfPRV5Q7iE3TpOzaM4UXNqUEB7LQUYk0WQS9tBtIcngpyY6aSX1+bvTV5SBh75QKxI+KBXtoPcnVjcoxuzJ9OVtXYT0ZsaLN5m8gQxR7Frd8aqS1QgOu1bxYxTXDRufsJ/QYMiWMAOzn6ljYnCOnxia8BDr+BQzOG3+FAN8rlfGNZS4fjmxiyDxGXg1TienK8bPgRJktc+HC4CVrEiYEtxUeqqf/yS+E32tE//nw6D/5cvproHAtr3xdnP+9XXw/gweZ3KMnTFhfHsy/z/O3qRJkizLzvvufYOqmrnHVJlVBbIaoJAbipBc8P/vueKCK4gQRDcIEIXqrhwiPNzMVPVNl4v7PLK5YLUgpCQlc1EZ6YO96dxzvlMqR+30FNHnC/mSuOTO794HPlwy/3RbeQwgFD9lrSC47J6umZgzIkozOOvNEwVrIcSBrjabsSPkxY3EcXGa9v3uB5QIwzJjRNATjRWbxbjVnG5u3TBJjBadon4MWCOyJdoZGKXS7l/ccTNRFbIksOZe4FksqXlFsyLvlTGUWgLt84325U7ngk1OKwijdEYzqO7GSUvyOdj+Sv3pFWud/HFFNDCqQVNC91Mu5cQYldEG6Ty9ovDib/DyEtxpJE71C9FbokOIfPr4hKCcDwdlS4lcPmW232VoT1gX7l9eac04j4i1jhWwUBmxch6V9hishxFFyR+efVZrgSVGrkviJBMssbdfsVqJIRBDZLku1Na5PfxG04fPLK0Jt88/UwJ8NTetPCqsIbDHxNnNWb3i0TiNkYDSeuI8OrfPBVMIq/twh0LtFUZ1rGkIhC2hXYghgPqGdJ7N2bE9uJq6rNgx4KhYP+m8hd0FnQv68dIJqlw2PFZXiz+temOUBbFITX66B43+vtXoIyu8FS8k5Xz46O1x+ycXrrI4nUEjEly1Phe+/80poiRNvM9P/HDN/N0PK//4+YXeb9TSadXnWypCDB4v0xDn29T/w3X6Vxk+NP9ts/CTV6J6ur3pLE6aUje+I6ouhBRJIyHBrWPfjE/qMncf5iWpzflCugRPOcTgA+EpnfupFnwBGhM4zCxeNWwNLlDZHEr38W13M/Gd2RS/IrVOr4Py6PR7Y+ydEV30EQcHedxtyDcSgYYA1Rh9uGm9uwNG3oQx/HQNqkRRKjpPtXmyCD7UnmMYieKWw/gGO3bF2Qx6ry7hTydRSAlsimP4cWn1bRTj5EHDK/lG8wa0Ifj7WphvvFkB0IHRUXO4ScqBFAPrtiK1Ektxc8rk42p4CwQY1W/61O4a5yHGxCShAQ/Yrw5bbrVM91D7FkZo7gPxW8ysMJRpBoAxf7c+khrmKrb5x22+bf0vf9wPMOcqvQWglTcrqYtTY5IJbfR5w/GQNXOU41EjH8UxP99jRt/8Z3k643bEOYYBRH1W+8/Epv/qxZlZ+BCf+F/e/2v++7/7I//r//w/8L/923/L//H3/57j1//CcTsdSpwTq63TbC5sMaMh8OUcvPZKvRd0r2T1xq9TPS5jeqLbQnqK9Ffx8Fz3xMSw4ol6eU9aFduC+z17R8cJNgiL0TvUvcFZ4VEIHxPpfUKfnCPTX+6eaxwBcMXWq/oi7X5zQ3/0HJ/88R3WlP4qjP3Azruzc6cyPQS6FSiD8nJS78b+i0EpSO3ouwNZqvc09MF4HF449LSSUiTF7B+UAaYFC40lLJ62aH66huvCsiRW3JbnrirfPL5dw1clbMkD79nxlGm4f3k0odXGfn8h58TT+5W4bsSwUeug743z6LRS6Hv192dX/7DqcAN8q4yc6MGmq8pYr04XqGmh1wfjOMl2ELXz/OEDeVl4ev+efX9Q20kphmhgWSNpAw2ZYXAvnW6DKp3mZ5c3jSP8cF3Ylsj26ZlaK//5P/2Z8ugYXtEXL4HH58F5wPqsTnzPK0pALNGrUPdBbYPeC3UXWgvIBK+1/XCBqDWgegVDiKhmlvgOSyB8QaKRnoXROvV+9xGQCukpzrbx6uM482v4sISNhI3oUUct1N3zvEEeGEIbeYqF+q1LlQH6N662f3txxkyISpMbpz2414PRjNzjhHQZKXnnxhYWyhi8jEo7odCQsaMhzOCvK6x+IPrOGESImoiSkd5mgapgKiQJpBRQjbMAyYe/tRltb3NXMv8Gx3SRLBHN3ldpXf4/GBGfXQ+snYxW6K35aGL4ny2ihC3SG/Td0OCnlMToVq7glq2xF0ZrtNc77SH0VyUwUfvVTxmPYAHLFWJAYyaEyaaJ4jULeYEwua+zTViTkrI3diFvka95iwBn1gJpEVKKLCF7DhZzY78NzuPuyrDMhmagl0a/Ffqrh5LdPej0QTNjxOZG8xgIw2stNDl1oPZ5OIgrxsE6ZyuUuk8Dd2T0SC9C+3rSjsI4TocKxOgLgO5OMgk8bS4I3twR59A089HCp0+feP985fd/+Fcc5eTxWnkND87zK8Ma5SheLNVgNHWL0XjjwT4Y1ein/5kmRitGrV5p4YzgBnQ0tuk4UldfrdOkInhTO1Ewdftir0a4JocHJP9dWfXLqOpwWybdEZ1Nndo4+4REBV0390IvXnJEyIQ4I479n98k/ysX57ZcSEnZ+SdeSuQfv/6O8jhZz0gOQl5gW5UlJH5cf+DzUfnl81eP/TwOajpQNer+BrnylaJxJUbISchxI7Ig7Q61EZJ7FkPygXaMmZSmbyF6Lu/2+aQf058r4updVNhWwnVB18h5iF9/+tSFFpnp+Dv93Gnn4TEhImqLC1/PrrTWW/G1kQJ6XZBlcexJG9SXG+04ePzyhXEExn1FVwiLkU4jdEjP66RBbP75STJlfEGzoQmWcoFmxMWRoxJxImHMBD0xra7Ro/ThyZ1Q+yTTB9aU2eJGkhO1yuc6aH3Qj7mrR7coFjr5dpJuhfZV3MO7CRYCaYEhlR4qohkN0XEy3UPfoA55U7jOHGoalVYf7MdX0vYDIay0M8EB++c7Z7nT9zvCSk4rUQ+CDYJ9IoWFpyuc0uhDfIMdndSFFeVf/+lP/PEPf+R//O/+J+77ztdH55/++hOve+HcXzhfX6iH0lugHhdUAvVQsMLQz0jPaFsZqzHSoByDchh6senjfSChE2KDEmHEeYUfFA4Pgy/V7ZK60segnQfLx4X0fkNJyFDa6yRX5Oq9O1SsieOFWmNYIz+LFzRvHxx8vs1xUAvExY0Ode4R37U47bhxVuHPXXmcyj7g159feTlu3M5XjrrTgUM7bfzC/aict1cQI6g7a4YaRzlm7fhgqHiUSCIxJrdOHc0VLJq/O0MgxXfksHAJmRCMoB5clQCVQhXBluzbb3Jblem8+xehP5wLu2RXPnWNWO3U/WCcHYpO5dcN4HpRQvRQdn/xjJ/Od4o0tydaM4dKiYswmh3BkbdIXgNp87RBfLqgqkRzRtI5hGCB2CISfDwyrj5KITri0YZbzjywm4HkM+E+nMUkRhLcxB5xRo81Qg7Oza1uaFCCEwtWJUgiEtiWxHOOvFinFkO6Y0iDzqaxtNK6UguTBvCJLgAAIABJREFUz+vuF/HXGFKF9lqowTjj4HgU9n2gcqLR/FkA0Ct11Gm168DpV0hxN9F1vfDf/vDE2Tt8vbHXO/fjlXdr5H1M/OnHf8Mff/gT8jXSvxrhpqSm5FUZfaH3JxYRQp8h/dGQ9opIR1PDGk6sr3ea3Wn3yKhC8z2O0SMSAyHlGTubdAqBkN1xFvKckVYPhg9phDaQw28bZp6q0WCkayKukfQkSANp0zk0mE3ZrmFoDFzTQi2dYgWKYXWKtt97cnLeORFuN+H1qNz6g/PeKGfjUe+UWimmiHbu1jj3Sn3cCBpJIXJ2o6lxlhujN4cZBcVSRvErnTagTK4HbeLrAyk+kUNi0+SzPhksmnx3E3cl9Zy8ZGjBjQsEBEUa9KPRWmdbHQili9K6v7VG7dDkGygqrtmN4qm54HBrKOM3tErv0xZoPhCX4T5fE3QE8prJWyY9rYQlEa8rqkLsbmA4DyVYJPWMhAKhMTaPy4zhY5zp2ptw/um9HTvWhxP6dLptgiDJ62KDNULym4Lcm3NvxcUMXSLRlNiVdYlcr5lb99oAbi6kfPMRRx+vtGqznGnGu0TdmNah3wotDkrqnI/GsRtrOElj0v4E5xpZm+ZKn/+M7lnPtES2p4U//PCJsw6OmnlBOMudpxz5tC384dN/w+8//h2P//CF/rmjDyE0IWWl1UxsTsILZtx3rz2UXgnBo2wdL5g6j51a7/THho1IW32kNsypB4TA0EKjOEhRQScFMeTgdMFTfAOTgTZDC3T1249ZhwDxsjgg7KpwClbw32P3sZWq50+DKZe4sLedNsp8colPer735NwFRh2Ur4MkF3j/jjgGymBfDIkH5znBvC+/OsF8QImG5YHEjGpgtDjfOBt5W3j+/Tv/ZYpx/nrQXwsj+25THa1HqTuMRowwjoq16qkIhKe0UcPgVYc3cu0z/T86liIWAiH6cDgtV5+vHg28kd4VzAna0qxc//SR8C6jPwzi+pXjHz47ckWz75QDgvhuGXOmV6Udz0gOpA8O2U6a0XwisRD6A2niLJ0uaFE0u8wuwfy0XlafRerGGJ1ynOgQznP/Fugu3Q3VEhOahbgxG54Ta8ruoMkraOISf6FZI6wrQQNrjCwqXERYrhfqZaX+9DPtthN79VPiOc3PklHawX7fZw8NINXxGmFxM/sChEGJhvbIqoPw5OVD1+tHL8e1wv44uP/6lT77X954vLe/fiV8bdTlvZNzaycU2I7Isl6I4cJ/+Pf/jj//p//MX/7h3/F47Px8P7n3E23ZbwalfCM4hsUN5OZrH8vRww7xpJnRSkasIqNQXuocTyeIMA6/EdHtm2m99upxiqbQKr3dCJty+fiRvF4I2SmThpC5EHNkeb56tX0Wx8H2AqkgodP7Rxx+XUErvRX6edIeBzIV4jBO9G8MU/6FPKe3Avcy/NY54lSQzVVPHYxzOPV7P2cyItLVEB3EgCuRs2lYUyYuC5fnqzd2FW/COs/qsOKoHi43o7dKA6omZ8IcpzNuRIkxOY7TqjuFGn55bzbTAfLNsCAhIQL9OJ3GVufQOgQ0zXfB00J8txCeYNxP0upHwZDgka7xNlJwIBbmsC1NkXRZiJYII/qpqAMdPjsbzbshtYPi11NmYW+cowkN5nax1h2l2Ru9nE5V0AVTT2GAf71htkSHmIhLRnQBgl95dbgAJYEsvjjXKN5XmhytwZihAfc2usjVfMzReiXaTF1UR2vo9IxKEkyNpgLRZ3lE/wTFZSGG6IJvG5h4aa0LDD4iKvvOXo3z9aAHZbSOdCMP9RuVJH799Rds/Mz//Zf/i7NWujxTbAonbY62opvsQ3RLpuVZ0jFFIAttmkACDq3qjHJg5qkjhzK80Qt4a2/w9BJ+ZbYOo1diyMSLhzck6vSzmD9rciKk1WEN6mKnF1sNECfXjxEgnBie0+21Ms7qm7KAzq/vuxYny0aInac/FLbnga6vtOEM1iQnYo1lWSga+KW9n3k1nY1jjRA7GgYxOkaEJ0HWQRxeXMrXB+N80DmRfIEU6XdPSox9h6VR3wvVDpoeVFanzScfxWhvLk9HnXzbznl22t3c5xqU851jK7/8/d/Tdqi3RPqwEN9nxiIQoNav2JEIrxtWYFzeebXcEKR7csUsepBa3ZCw5HekLbB9TIzXyrifriynRBgX6EqV4t9/GGjGM6luViYcFbQhaQEiIWwMq4xxeh9lH2gySEbKlZgNXa8+SzsMckDixrUlchM6gcMC56+VQCOniq0bt6d3/N4ufGKFHy68Xo3/5+eTNgbb7pa7oQNdjOVdRGVBSYzaKaWyjxdiUi4pezC8Dc6zUOuJtSd6DDyNwZCDx/6PnEVoeyJtmXVzc4PRea0v3I8v/O//50FKmXi9kFfhw4+JYfDltdJ++gu1VX46BqUNzvKZWgrn/eFDF2lkfUZl4WkNGEal00pn/3pgmolPkUxFcue8Na8OvBvSIYwEA6qTih1p8sD9vKn5RCC984lC7bTpQrLrIC6NfHVveX8kOAPja0VSQNaIPSq2N8b0RFf7QuiCVKMfgeMY9MdBvxW2bSHmTLqI98V+z+J0+5aS10TKkRCUpt4ZovOV5zvATMHPxanD3Imm/ogSFT9xFcCcCVsK/Sx0G65of3NIyqTCzesKb4q50cw1txTcuO1JhOkFnFUOw9zCF9VpB613aqmc95NRlNHDN5i0Vb/i1NeDMSrSOvW1eDHPm6e2DCf3ncPHNinCJOcFD78wHQ0EiU4FGFNlYI4fZjLDbIB5cHLQ5ztTf/tm/f/sokWM2KzXi+p0BsRnYzZwZGEXtBmxOWxapgXPPQZeWdiK3yjydB6FmDA5/T89mOQ+HM+ZfOYnkpB+YqPTR8O6UKq50f7odKsMc3N4x2inJ4qOo1Cb4i/nObCXN8uhlyzdykEeg6eUGEkxC5TaGBXK40GrlbN0Sh+ctdFK86dOGPOPnTKV+ufLkru/hPnzUXFSRDQkBrSHt5mNfxFvOMpvvzd/U5v6Pw/xv8voribX4V5pFVL3yg7jN2aUzAnEKM1TLmEympzxgHXoNrx+fnRH5TCDApPY8H2L81FJKfLu4weu65VL+shIO6Udc9AOe6vUMYjqLVGiPjszMWd/Lko6PBAczRhn4fPtRq+dUhslZtrzxQt2inAJFY2GPq9oyEjc6AXmx5kufTpCBDM3LJipL87orpUYOvmpodH4en9QbpWyL5AC+vsFW4yhJ/2fDE7Yf/mKLQO7nLTD2P/a3UK2xG8lsuNrIQ6wP7xHYyRKRc4Av2TimEzW4AaHNpum1BqqRkrQbNCOgZgryoUbWHVQlAbk8vSNhZpSIjy5P1hQtuTijJ2OKpFic+c4/cPTvRF7oKSrXyVfo9LKyfnTT/zBCjqeKIeylxU9fyVaR9cnPzkxgi6ksKLLhoaIoYxakGOn98qXX18pX0/2n3auPy5sHzMjdlpo3F6/MAZ8rqDaSenmPucyCLrMufYnJImfNHOT2e+N++Ok1geteSB5jMbr3YU5JDpeJgViWog5omlzzSAPRM27dKJSa6N1ozUjWJtX3Qs9Zuoy/bXnjgQlZm8m8+vtfHJN7Gofh19LY3FRqw7q1wG3QS+BmANhdcTpvhv28GeYzJ6URS8EySx50jpOpyWIVMZmjC3Smqez6l8ro3/nybnmTl4T13dPrHEjSkLM1Sa6uHS9V1pzboqEgAZHA7rFTOdJOKV28V2nE2kCne6xsamCgQ/XQ4CQkr9fHeGHDG/7MsyLoWFa7/AdycYk0syRTZ/zq73TzuaCRYroJRGTECKMpbmxuY0J2HNqg/QB2mZi3nm0aJgWvsVB10G8UEldeHpjGjkM+XDjdZ+KZ0hedDNs8oOck4sZKt1/RualvtLn6CdBsIwSWDc3w1ed7Bv120qcW1ahUYdrBEuYdYRJ6aViZecomdeSeRyVozQGTkaEaWv0OTrSDAltfl1jhtkDTc0TRCGR1kHMCyHO/OMwKsX7SpojYFJ0g4g1kOzvVsN/FjEGwrwuju6GhjbcLCH0GQdkljONWRbl1j5XkMe3xS0iDidTIS7BRxTDsBAZYaDRD4yYfaQRdPKQglMNzJT+hvYZ9s84SG7LC0FRUR8JmtAfA9qbfQ/vXXXkoSNJpqkkRv12k/Ss2fBRy5uZRgyj0Xqjt+9cnB8/VZbLhR//9CN6RvQ1IHVn7Oaxmio8fr7T2luhi6BbwLJha0KGiyE2irtcwnuGBDw5c1LG8NnkxFciwvZ8IQYhpUzpcKviP5Dq7cPDjBqiizzTF9vw6nrHLQ60D8q9Mkbn/mvFqnFdMnrNhA9Xv95p5AgPrBa4FawNWokwKlFfsdAZccyW6QRXPwF0/UBKypoqIRgxMPN5kTpNAOX+Gdog2oUgKyFfCFbcQlZ9YZ7Fw+PLilcOWJ9KoRCeI+EpsI6VJIkPH68gcHu80qpROqSUuAZlH4WHnezDzd2fkru6elZ47OyPn/m8B/oS+fllZz8rbfWF50kQXGwpHTmHs2gDxCUgGXJeaDUxupCeL1zXWcWxquNn+uCI9zmwH8TUYWn0U2mHIu8UFg/MqyhLvBKDO23aUXnsN0bMjBR9UxzmfmszYizY6ISJN/F+2ODX0u4wskUGqsZy9Q1TTLwPVUDGibbBGjKuVoXfPNUjOpZkmaOs0xCUuC6o4sFzEoFMfzTG0SmlzbFWwVKc2d6BWiHmK3m5sF4SabaHj+67jAxDT8FywEIC7picPtpr36vWlkQPgf5q1P2gfjl43B+cZadb9xrw4OyWsAmSYXB4yFSFrJk4AufipUH61sFNhCY0G9/qAqFgBkdNxC70Y6dLQMLqu+0a6af3g4Tg57EjHQc6vCey9+Ez0zoY0c9QXczTY10g+9yyHLsLBV5YSF4TA2G0SRW0+d5LETvdkJCCg8XyJRJDIKnO6sDxtvU6tKlByiuSjSU68sMFS0XUjeAx90kzhLAkB0YvrrKKBDcWhOi2NoN2mxzcfRAMNlHeXTI/fLzyy0MZZ+FqFTH44dOVkBJNNpYe6e8PQl5pptOsXj3kLR5wF2Ga1P0ZO4aXB0VzM72GK2bmP8c+IE5PbOn+L4AbFXRC+QJYemPziveHqqNbRIUW3CoYl5UosFmfqRRvnCMo8clvISnYDCEoNkukaG65C8M82B1mca8tqA5iGqTub1myOrMnLTA/K73MmxReZYk6WaHYzaOK9919LdLRBD15lYRmCGsmJI/3OSYnupttNrJpEESi2xq7ay8uFYjPV8Xn6jLLv8JF3fH/PYuznplApL4Myu3B7edfuPXKPppn8GYZixAIT2DaGTxIkomaWXUhkmlLgdEJ6j9QVafOKebpjSGIFkyMoyo6hHweSFrQ59XdFjnxMK+Ek/lGYHZOysSXjD6w0rFjMDZhBOe0iJkLR8GFknK7U19uxEtAc2B7f4EQaEUdiRkTQxIiK+wnWipJHSmZr8kjUi0jzA+7zDbQZkiB9LQRknC9bBhKbUw0RfZro+g3KJhkL8bNm79jooTZAZqITQjDqF+r/xKP4bbHq/LhKfPHf/WO8rJSHpWn44Vog9//+ExMG61/YCFT9gOTRDNX0cOovwkgobkQpHP+F4V+L1jdYSTQTNQP2LwVWXdRox6ddnRviBbH16goMTRPc2QlhDcaoqIaWKZpv8WGpMS2XUjB3Uz9flL34jY3FcbmOd/sEwfEhKZCE4Fbw85BnMJX00G3iLAStEIu9KEY0+kzBkG94mO0MsPljRgyKSygCyLCMY7ZUXonmD+X9CKEp+wCUzDnP0XvRhExf66E4N/jxKr6IDahMwGjb9+DzGeQBIZGhhjh6pvvdy3On355JerO6+fTr5TnyalQFKo5yzXn4SHYJTk/lXfkHFhWn7UF83v8wDB1Wd2Kx8X0skHz5I76c5Hz7o95yUaIRsodjkY/GtqVZNnjYCro0XysVAMBIWdo1dVazU5B6CRsGH2S8SiCsBAT5G0lrhFZk1+79wrSGZroZdD2G/blxB4NffpAipFE8P8F8xlhH5SjUs+KHK6aPq0fyJfMu3fP9Nq5fd2/xaSiBDRC3BLaI60vjKaUHa7PmXc/XihdqR32L3f6XtEmPmelkSwSy8pxL7x86dR7R49OPA7UBq+3SM7Caq+M8qCMTj+r50UDhKfFkxQCx+6jzi25mCINTJ7ocaWvDrOqBz6DPU9onlj5plBL94dgdxEqfli8pnBp/jMfRmgJWvRru/rVVgHOHe0PonwlL4JFRXGfXTYlBuX5+epFA3FwPxuPo2PpDYfp3KTampf9lu6LKE1LXDDXLLowbPFs7cvJ2EH2QM9GiZ267/PrHKgJGlewSYochpynUxSTEJZIiomcN1+IcVYq1kbQ5N/DEoiLTi+2R5gN8Xewit+YasNa85P2b4TG/jZ97+aQoofdZyu00JLQknIOV/QW9YFwiBGTCLb6mzEKgYkFnAPpgdfQW/NrleYMdKx52YyZ0Q93qvfVr0g+pB+Ms6MSPAkfdV4t+uSXelmMRKNHH5JrVCx6J6OJOVl7qMfSiGgS0nIlrslrz80Bt/Y2QD4b/XbA44Sjo1c8MymziVvc92A2aLVz7pVY3UAYUiItK+vlQj0r4fWcHZ3zezIlxOhvp5bwdmkQTWzPC7YP6j4o5aQ+drdVCMRsiCVGV8pZud+aV9+VgTb3gj0O6H2QORn1pJujL/vR0Gd1tVQjNqDu1U8EcbOEdVC8R8aC+Yepmtsdz+ZxkNqRKMQgU+YbXgchim7pW+nPmLFKawFG+HZr8lZyQVpFrAAHMS2M6FQzG8Fr6UXZttVB0LlREY5q2OyzcHK80EuYgpaTGiSZi30K2l3Es56B6s6gU7xHU/EaiMMNIDqdW4SEDa+HkGGEVjEiFqPbImMgpRUJ0EP9Nvu15FMDSeJspYFneecvb0R10XHe8EYf/ozR71yc5TwQjfR0xaQx5GDcTkatyPWCpsReF0+ii4J425gVxXYlJJ811pu65e3L4Q/kECAvhG0jhOlh3a7TGtXQGNj++JGIsY5BTQ25KpfwnqGRZjdGq9Sy0ztObfO4pge8xyDWA5pRzzILkbLL3b0SFyVeleUHzwjujzvtPLFS/E0cAlYK4/WGyoZsVzStxBRZQ0GjT7H6Xjhfd3d91IKkhMXA6/6Vw3ZHrAzj0MbRCvu9sN0gI8gS55WyOAQrr5goxw3uPz24f75Tby9YraRPn3wjW1dab7yWO/X1V173nZg3NCbaIowBtz//R1Tgr9oZQUlL4lmUvEZ+DSe7FMrd6G0g9UENyp114luGU/eHMH7yjo9QXcwpNH9XroElLcSQOLsze+vutP1lu0AXZFeC+O9ekVmp5yVC758+sGrgowi3e+DXG9PIDyMyQXEdE6W8RkQ7JoXzXmiP4m9Zcdxq97gpRsP0QavC2IWR3dsqPbj6PVydDh8WOCOWwtuwmLA0L4RaJ3+2hylAJqidUQpWBxI77XcHYTPGus3g9USR6EqMF2LakMdgnJO+IYZebYY9gF3QO+QcCRejd7eHftfiNBrgmP5hswyoNjgL+WlzMFJ1+doH+z5KaBIoFgjmCYoxklPojgFi6BrmVdYn1CLmj32UkJqPEpYF7R3uu48gxCZQOnq78zC30xpMx94c7c4de7j45CMVFxjesJwiTo3TRdBVsd28sOjtT/AtbtLZ9NvbQoMSgn+9bcyxyTmr3IXZrAxtVCjGse+YQa1GbZVWC92Ubo64kMi3CgmCw6fOR+G87Zwvd6+MZ/j8Mwa38s1R0tkro8JGJJlOWCjU44FgFOmkdWVZVzYNbAYvrWLdbXOeDe0zCOzXrfbGtTGl1+7OmakvjMD0AvuYIKZEs9NxnjZmPnIO9av5KfPGRnqrM1AlaiarssrgYUo91UPk8Y3r44RFfy4U3EBa/bNT8ToOlam4+nv0jYxnQxglfPtUO5bJbzkEQVe3FYYKNtpU9j0zK9HZTz7vdKuiegsv35rhx3SAhBlAULdyijoBJMToBP3RJ5vKky6/URtAuoO6Y05OmPzexRmfK6MLpXTqWTkfhaBC0JXf/fgj13dPyM83R148C/Vo3H6+0UvmjAtpApnYKpYGpXSQRErPfrU5nYAGhWHuOYr99BlbqbT7Sf3LrxQdlDCQ/gUzoS4HQzq1exUBab4rz+HU9QY9rX5K5+qOpe6h7DALdW0oI0Ff4JSNJhHWw32xbXgaYiwkS0SJXDbYrkJcn3wRfblT74PztSDXjL57K2/1xT3Oyu3FIWFHnYNE69hlxXKmjhOGovHiqRCN3F9vvP6XnzlfdsrryfLDJ+L1ia4OmEqvzfOcy4U+lL0J44S4e3ICAdscenaUxo8X5V8/XVnW96T8xE9//gfk9YVr7FgCfVqpPbLv2Xm4ZWdZfU73qN2vYPPyahOaZgRICXImSHLmayiYNdr5eXbEGGm7sjxdaf2gj5NxBuzsVD1pEXo6ub3c+Md/+uwi2xqpwRlCEaffvxwv+Es0kpaFbX3nvl0T1O4+P+xOURg507r8lhoxP/W1GenTwhAhHpl2NE4p8EbBvblZQjhdtNkWbBL+TRJIIcRMiIn8PhAvoO8rISRCX9Hm6Z71mljeJXR/Q7+e/nMrvsSi+W3FwiAsF/dkj+1vLsF/4eR0knXv3jKm4hVqmpWcMktM6DpnSJdIQCiLd4t0DcTJvZcwz6TodQdRZvmLTLrZdGu4F9C3vN4K1k7aOPGkJ1Cdd9PTcIvqGx+mu5HASv9n0rTviPMf/WufO7lMpL6bpcf0WboyNWbMyRPqSshK2pxrE+bsqtdBO6vDurwfD7/A9YmVcYuezzQ7rTbPZKp/+Ibg5U7Dh7UyRzGj+gelmzEikKZhXedpH/xECsuKVU/k2CyRIvk1S0UnhNuB2zZnulkCiwZyDF6lLi5SyKyPMPGMpKif5jpT+kN0OmDcJALfJNTphPP3lKvl+ONY3brI8E4TBf+a6JT6QAfce+Ps1Yl86mynMTqD4SfPFJQ8B+JzT5lCzTD5pthL8O9Rk3f0aDSGjG9hBRHcVonzijUpctX584H2cHJjmIXAIQdGmK2AYphGYsrE6D09muY1Db6ZFN6YQzKv8i5GvfkWnV74FsNTHVPhTT5yk+9cnK0t9DY49i+kGHl6l+G6odeFa154JpA/fvAimeeN87iTdHBrkdea0VCIozOyuyN4CoQReDajhUpJ3mDce0fN5WmWiAmU+2d6OTnzjW5Km5Bgq4ZuzgcyfFFyVMbRaY/q9Q5BsdAggE3SdkrTYoIPwEUC3uQ5YLxiVui2M5o5i7UN0MDyKfL0Q2J9uhI1c/+lUR8n++cbZgVCxUamH0ZahxfjXi6MIey/vnhOsneidkJoDHFuTj92bAx/m4U5Cx2G6AZPAZ4W9P0zYb2gk+MTr4MQF+L6Abk9sPpKyJ2gQtw2RCPchV4adXSqJX6VjbUZl37wMQlcEl8lUmzwaN45cwlGS0q1xYmHARaN9CEcYUz/aXdItMjczIoH0jvES3ZyZwn+M1+E3hfaSyCmSAwGVLo1ft3/TDDltSYeDPikkDcIFzhfoZ20ffc9YPUxRzd/d9KM81aotXN5/0RMEXmKbkyIAY2DoZ3W77TeiMl9tKF4ckd6QxZFPyV6eaUV4fxSOWsnPc0Z5hX6GJRkGF7TsW1XlmUjXhcXomrBTEkXF0JjTH4drg6p1jgwS9hQdHj+12IjSCfSWYOyhsQPH59Zcv6+xSltKlYxEtdMftqcCh5nXjJByJ73k1aR7iFUHYOgdb5pOjodPWnphCFOZA86YzgRM39LqkyrGlDPMbGDPkeNErxAJ7pEbghdFKNi/WCIOOlZnZ8KbigQ5i7rzzXGwPEjS/QeEBNXZvdKP6bCFiOSO7JOo7skyl6pvXP7641aGq0WXzDr4rBi6dTqI4SRTozpDglKCishD2LuXlUeksO3h2Epe/SJ6tY0hCCO9VRcwBKxuVtnj+3tnVAHyYS8+Im+5AVFKXvzn21y6jz9wSDQVOkqjLhg1d+KVgZRlctzolbjLBCCfx02vB6i1e4JmeZsHZmJoG6G5Owz5tEco9I64ODlUTutHjw9R9Zr5PWI1N45znmLSn7qrLOfxqT7p1ECMqKPytbFB/cIDJnq+MzuhikkRaYa7z7lgDnCVCPN/Lmjkz+lMwca10DRjMnKcvFI2vIuOCVQvJSpL24bNfPCppiVNc2Ze0j+uw/+bAs6p0pvoSNRMK+J0Dim2d3rMlS8eSxsiSC+Vr5vcZ6VIIG8LizPG9sPz1PIMXQRWIX4vDlg65dfkGn90tFIvSLSQSBEhxyt4YAhnlyQgGoiTNOQ4z8Cz88bAjzqDgp76ER1+d9HHsY5F7iLIIPe76AZts13XAGkfPM8qiiaJn6yg+RAeM7epG1Guxfa60nf/UoaLhtKnSyjBSFzfzloj8Kv//EvWDfiuwvLdSFfrhyj0UfxKFU3ouEqnjj2Ii+bs5GyIW+0htX/27ZlB5+NA+2Gqns6RfGqxNr934kRGyvSPG6mYxBtzmqfV66syDBeHzuOD139elpe6BFKgBKeKHKh79V7TY7Ges387oeNo0T2I9KaG90tJEYzyv3AWmdpc+Nd1MdHZqR1Q0Xprw+ojVRO+gjUkGhnZZw7z+9+4NPvr8jLyeNonC2C+QmTpx+1NqON00n3FrDks9iwXH0Mpwq7f71YRyj4Ny+k6HnYWg5oQhpCzIrFhTOah3TMr8BR3IO7XgKPuNKjcnk/yKmyfFxAXEvoIowcXD4eibwl8hJ43i4scSGvnSGNkwdzsuOSQoE6Z8jJsvNqtzvNoBVFw0KaGJv4lNE6PET+PYszffDxhqkjGGWAdjehmwZG9Fo4Bhx3ODq0WYyagziAOUau69Xh1DXQ+qDUjqvLwT2yAeIQokKOyY84b4MjZT8xx6xVH8PRHmPYjIA1wGesQ9K03xmak6c9Up7vIN+Bo4qPUp6uIprpAAAgAElEQVTUhSptPndqeBeIM/79VhCcprbfTvafXmj3k3KeiEZySNiQeaJWTxqcrljm3H2M8OhITHRZyOaiQFgSGiNsXmunaoCbzd1Jwm/zr+bftzxHZ/cG3DQeq58+slANX2z7A2mdUtz+f8nK05Z5fnoGIjeLHJ9v1PNgHAc2KjKajxtOWGVlWS/cSuKoJ+0sUDphuGF9WQJh8TrGfjqz95KCVzYsgsUAeUVChLyRlkamwRopBsfLznk4/iUkJVwSAfVbgnmnTIzBea7VTy2aO2yC+lw1rhHjQu2Lu6hUCeLhhHYOrPhfLBFJAtWFJc3mVssZbmectDKou/B03QiXTHi3uiuo+rU2ivkxaJC3SFgDXSrVjDUG1KAegDZ6qtAT1iOjjhmWeEwWcCWGyPOS5rMK7LFTzoO/ftmR+p0nZ3reJqzJdzgZU5qeyoHN6wLdOB9QhjODJEAKgqQFSZl1uZCCEgVKqVRuiHkl/ZvGMJsAScFDtC5MeP9mbYrNDiQb5qHk7ovTnDuCEfHmM38jiTpiMsTV1bzuzNygELJ7gW1pDIonGbq4jU3erqOOzu+l00rl/vmVetvptRGzX5swoZ6NViutFQ9Jm/hbTAd6FE/KJ/fEypj/7RAY0a+hOg3x3xCJ4uqfiThucQiaozOBgpvkPRURGCH5KONs1JcXrDYIV0IMbJuwLYnL5UptK3tbKMcr7fZgDOe1iHYvvj0hL4m0bjgqKUDz+Z6ai2kpR19UUelHo9dOILJooiUYUTGc3JDiwpICa4qQA8XgvJ3UvSJPyTWKLXsyZATqWWgzHI8q2iMMcy1CBhnPs8aotLD5GzjOq6/477UXg9KR6sRpSXFGNp079Aalhob1Qq9KPYT3z5nLInDZPHJ2d4U9fksFdGc0LZFe/akW4uY+6sos1p2foYFzgbq51VFtkuoDW470OaGz/aS2xv0vf6Uf5fsW57as7pbo7ntFgOQI+nK6O6KNB9YGd3v4nCpBumTyZSGEC1Ez16RztvOBcBzc77vbnvqgns4GbdOHGH/6DAq1eZfJCOJm9mIEO8AaNlM9LXhKpcuGtc44vnpPSIhoTJC8xWm0TrsXgr0lJAMhDc4caJKI7xIi4ju3mTdMoYShfl3OAc0Luhjh0okpoptfmd+8qTkoQ33wqnUqdMUfWLFWfzN1OJNX0lt/RWQQ37kQseSLx5K6/FaNEj0aNWpl0GgMVBIjrZNsrq5Sd+Fowuji7iPEnTxHQ24HlzFYRuVpNWLInnGkw/nAhvHLl888bxeea8MeB3YW9q+vnLUA3kDW0woM5HQlVdQQGiKwbs+M4cRD9wztLH6x5nwpHNac4RaV7f1CTIHs3fFYiGjsWFTa4U6tGD16uMSFNQnPl7lZqVB/3Rm7O4aGKmeKtOHVGISArc7B5RS4D7eHbhE0UgpwdI7bg6ELa1zZPrxju2bGcSC1TuCaeAXIql6vsKxIjMADxc0dpgI5gSRUNlfOmyGzR0XC5s8rTsIibM+Dc4d2c0qHIOTLSk/fqdZG9aKbgNsnm9gkDkzvYR/U068kx6hI8BNQUySuCwmfEb4ZeELIztExvnVu9jZ812MgAsfjcGuUzFvFvLZY7wQ6IoMxoz9v/tYu0fGVvXqvh3gCwmSyUZvTDJyFozMT6O+RjiBZCau6uNCFceApgikmITPdHjxoG1Lw+JLMEYf6L0sCoE7Ae2vd8rrA4XlUEaw2H3DXB6LDM6IaSboyhv9Zaj5OcTI4Hg7ow0UoxTs0jbnIbabtfdzkNQ9GakKpg6MMFnPodYrCCIGRA2qDSMVK4zxP1piw1LwWsPptobXqcTnEi3nMKy9mpwNvWIYQFR2OjDQbjo60QCBynJOna269C1mdnzTcdGJ+RQLRWXI0sKjTKC6kqCyL1yV3Ef9Z9u5JFZhBc6YA5zxeQ2GIizTNN1rMN8dRB2Pv2AohCZoSmhfsfiCtTzNNmJndQFqCY1BDoofd1SydH85ZjSkSGFSE9s2IInlCxGt1Q0YCOQZvnX9i4v7g/3/33r9QZFQLIQfy795xduVRFTtOxnFQl04TZTz6BEQNh3RtCzo2Ynsm1Ib0wpHdHXLHjQzH55OmRglGfwz6YQ7hjdB3r1pojrxDslArtA4xL+QohGXz08U+e+wM75jUEeYCMfr5YJyd+lKgQwyrJ+k/LIwQKPfB/cvBUQsUFxqG+wT8FxlhJEHKw0+hVtAupLx5W1cQVyrLYMxTK6ZIiJDfZ/86yo+ITYo6RrNBPe+MoxHfrllFGSGykIirkj9EPw3H4KxeCWg9UWvj9vIV6Ts6vrIsC+u2zHC6orkhvfH1eCWMiO5XFk1s2xPKzuCORU+aiHWiDj58fKbVTllOlm0lXRckNMa90X7JtIbb1gaUcyeJscy2NVXlOHywvi4FE6HqwTBjnHAO55+co9Js0IN3d45S3eFEdNsdlTF2hILMrk65JL9VlBtIorUrpTZq7ZzH3T3HeoUeiPvpZnKpSI7ounjxsAJPbkCP9vakGbRVqB+fkJggKrefX3j89AV5faXV5reFmEhPqyu3rXONwiadX6XRGaQeGd3f+ikZl9Rpl0RbV3puWDTvDR1GvBeQxFkv1PpgtAcPFrpF2nDd4rsWJ80gGho99aFVZ2mQU1BExa1lOIxGw9ss0QfYrTakNarhVxACpRS/ggab/BqfRblFDXqtrgb24Wrp5NG4Lu1vkmHqljKbAGQdroxqwHSAurm4jU7fq4doNx+eD4FxVsaolHrQeiPg72TfuV14MvM+Cxvmi3d4qZGG4MZ71418F1QftIcciUlIW5zvJ+cNUZ1t5NFVvzHYHOu06jC0ag1Jkazq1RHdSf9mhlUfT/TZ+xn6oIfmdIIx38hTTermTIHWBq0O2tk4rdDZqctKD97gFXT+eyqMMECVZkbtjdYKrXpRVVrUnyCtezmV/mbmGPizoTX/flrvk8Ao7tW1QRuNOsnuAm4LVFd8B96T2Ztvjk5BeCuqYtICYN93yuG85HKUSd4YiE52wqyA16CuWUw9SYPnJuc149v3+5ZqAlz4qg0OX+TGW6zR/+6lzcGN/DPsb6M7haNWH5XF4LwiC5wypp3RfrOYNhhlEgRHpw7HsPTht8jvWpxtnwttDKRCfnT2s1NrJ6aFsC5sF78yte4P4taNLo3DbhzHjfI45kIUCNt0+RRicBVuXBJsictTJgS44ZXqVoCQUF2JK26eNq8ef9x3Wuvsj+qeytXQkQghYblhoXPelHIq7WUQgrBdA6PD41bYX79wvL7QtgQ58PTHDdVI/Wtj1P+Xs7dZsiRHsvQ+VQVgdq+7R2ZVdw/5/u9EEYpwww2H091VWfHjfq+ZAVDlQuGRM4spysQiJKSyPCLujwFQqJ7znSAupaDYtjF/nMTXSfScN9qmWCtrNJOfUwLnhe31Rtkr25eGIZTvkz4nP+zKaiiCWgplGj7y7nZ8/cYVSu+Nt/HG7bc9BdWm6HxgZ2deLcuzvjJmquFlkfZ7YD1LY6MABzOcj3EQ7+CHE/0HMR7Utzdq2/nL/q9YVYp/YCghlet0juPBH//+d358/cH3v70zZ/CX7bdszoyRMsMtR0A4zNYIM85vPVEvx2L0bJUrYEgCnsfZiWPh2xSE/Pk5O2OebJtmya2RbpLIjfDcnevjB+//9f9lvJ/M94uyv2B1o9aBbDBfGoFQZ2NX4y7KdGOG8CwwbCb5gUB6+ket5CbJGJw/PhjPiy4HYSD3O7UYtzIJLvo4+e7Bx7nxfH9ATM6meHf692/c6iuvv/0bU4wpyvij05+TOd5hBv05aUVpV2fOi+EX5+PBecWf+NBfWZyhMAmuYzKvYHTHZydi4rPjQ9ZOlxuTR+CeORjiyhgwRoas5g72jrgnL4c04RqKhNDMKEW4amUgi60ijNEZI0neQxQJOI8zo9xH3jEkgTJ/ysYAfCKLG2OSdjTvqe+9vh1c358ppN7J1OsiDB8ppiadGXMoc/RsyMjkM+nt8yAPAS+rs6yRh7tnV5YFM/MVN6eaahKPQTAzGhGSq+uSPx+D/jzWhx8ZAPVJjLPAWt5zbCtQUhI2PbuD1vIkLLKsSXNwzYNHd5gnP7fw6Jx+ol3xuJg4HWd6x/3ieh7080w8S8KckggYSZYLUzwG4kvsL4kF8RmMOdBYHVcJ1JX+TIKeepIJPT43/M70njPVWQkt6zFNw0GoJATa57KrOFTBtsRK7ntDW56A2TsgaYHMJfsztFSKGFauZc6I/C6W0J1Cyu6KoFrXAVLT4LCuSykTdfJ+nS9vrmuHWn6ntezU9eq/ev7/jA4zuBwkJsM7fQbXpQyXVTXEPzs4/38WZyNvBV/PLEXPTmfi4ozjB8xCl21h/RX3wRgfoEkW6FfhOpXntwfeL+h/SzDybzeqbGy1ZoYIwr1WmhnsL5xlMmen987z48F1nvTrxO1OSKG/f8+YAv3CZzo2mt2okIROMQfqOQJQFeacXOfg49uD8/s7x/d32ptQXoTxLwIbnPNEiBU13vHrYp7vzH6kLMv05+qzabgGsX/2VgMZO+oFaZlcdXz9lo2UfaduhVYLkwdTJ9NSNWrbaxJE+8Gk8/GPr0RPWWJZc0PZs2wtliWZ3W8pIrg6vQdxBVudiEKzxKmd18nsD46jJ5HeKsKFa/B1fkWGcH1LtlNoEPNB+IPnj2+cx4He72hpxPaW7zk+oCpeK1ydmBcht2QReTpqjnkhU6kD9Jlp28/zZMzBdktnz5QG4YQnjjT8JGZeh3SV2n52EKPMF2xUNlXsxel35/ay0bbCb3+9Yyacz5M+nGPmSfzhHWIDqdS3F6wqteRY6DGy5Naq6AbahHkaaEG5Ear41qhM6jwZtrJ9cJyxNNNCP/Nkr7eNtr+wtd/ZfFB98N/m+zrEDsKDJ5UZ0OLguJzHR83vvkS2Ov7J8vznZa2O/KOuyelcZDNVRUi7y3NMlIlpSdvRCLIH2tP5vgXsg9Cxwlw1S2KtyXedTviEositcdMX7Bo89IOhKc3K7SqWd26Ru0XQRs6wJHMuVBoh2TVrVZiuyEvJHd6yKVirwf2Wf6aW5ND88UGYEGcaw6Uur6Uo0Rqxg46JiDJnQTDmdKSmtEuX/S07xHnHEYS6p9CAotRaaG2jW5qTrWfHObkzgrWNcDhmEL1Dv3Cz7FhGYhYDhQl+XUg4VZ1ZUmKWEPHAbImyi65gnuQRoTWphXNwHT+WEdo/Ua8gnmngpUKdWEm9qpflphkHwVwSyCQAPJ9P9LzwhzPnZIws77VKbiolQ5TNJ3ZL328Qa36Yd/XSjLYX9laotja8CDSHGfl+QuhMTAYvX27se+X+9gLAc1Hye4+Efo1sLKoGFhOLwLQmdeUl592zL5TNmUIDrRWRbS1OR2YwzgAtVNnSphjKwZX2sqrJE359I7bG4+MHH88HcTx5/HgwzsFWcszWdMMiOM+DfuaGai+Cbpq9kV+9c3ZNPyczmDMlW4VK0aSUu8N5fUAETVdZMSIH3N4JzbIxrlycMgMrQq1b6m2nQR/47EkwvzdeMIpcqB0J5ZLs+okHEiVrbVuYyp2FS8wBtra2shGD2oQSSuw1IyU8EnPSKmovlH3Dx4l7Z/ztYyVWNKiKviUP5nNxclfkscYhnoJwvya2kr8lDAldD/gA2fLBu22JFhaltcq25QIML6he4L6gWILedsYxOX9c0K/FENkyqm9V7eE5PuC6KApVF+1+wZtBPlMDEw6mmswbsxQtzIGPweNxEO4UTY10LZKVB4WodeWkVqQobjNL0HkkbaAHYU5o0B/ptdXFJe5iOZ5QxZrB1rBlbC57hjvNnqILDfI7q4XtXrhvJWfoqjy0QwjbpcxaUNsoMhgyePnrnfu9cdvSuucfT8YF1+W5MIemxFqDEgMLxbSlqOSVzN05On4mKgRNHyaW4c9jeyDd6c+gRKVxXyzr4EwxXD4j24a9/QsxO+/v33j+4yvP7995HzBC2d9eUW0034m4OI/3FFtcF+3LjbZlR/qXIwDLdkfVaGVn+uAaZ+5OASGdIO+gAimnQpKXqoHTaWo0FYYFsyr1L3/BSmV7eclhdQRz5iI/jhPHOb8/uI6T92//oHtmVWlVtpeNXgxXQa49Ix9MEtj1kTpekQPuAS1wckDsffnqSpqrWzXwC/xizjtzBlfP+dacHSSY3sErPlsCzPZAr/cVnXdmhHzRzMvY7+jIaHWtHTXPaD0R7PUG2YPEW+Z6RE8V0VazE4ukw8GfFelCDUkAVGuZR2rOrAcE9A/PIkBSdSK3kiGsmmYAyKwZIrm/pSrbTRkXmcs50mStkT5YLxWKQav4OfHD8WvHpvGJ848ykHCsFWxv1NcXZle8C/PIIGHqMyWTkjGKpQxqbey1Ip85IR8pKgk+wH1p1w0pDbEbur+gkl7U/seDiIyhGG4cUdIczeDj28nxMZjH3/ExOR+dCNhNcSaDge53yn5nxpN5dh7PZaQuhvpAt5ylxmjptEEzecCd+DiYY9CvTiMIkyVcEXq/mEwGG/igvD/p42Rc7zzfL57vIHtlW/wq0cBsLIFCiuGNZbe0stwrv1jWaslSZNvujFgql2tmLonP1VKeqxnDnw2C1W7eRSiqmb5sQr3dM4CntSSUj4mLMtXoPWXs/eOd8zg5n+9MSfeC6Q3dGtMsPYe+wn0k39w4JkT/0wFjBUibkM+x2uhZiphVNJJvOmY2fjiyLJPxjsdgREejgi+Xe1HUnhnsw0AJ1LbUapaaY6YALTOBVJKSN9vKohROospy+udLt6RRMmPgXogrgdKGMDRnnyHpSXVN2NTo68QhP5f83JeWUyRf79qJxVJq15owe2qRdbD0Y1lKxyLBYY0ZnXFdxFgaatK0MPXT/Z8Zp/pbwz/ycU1QG+mCIdIzW2RtUFBMEyPjwXWluwU7gWwWhS+OpjakbChJGRwfmWMyNRfN5QndtkhWU8Tg+e3v+BiUmUS87aVlupg6Wgu2bfjxgY/O85le19ueYze1lS4mioVmA3A6+CTOnmXyHIgbFj3HhAhzDjxR6GnK7xc+L+Z1cJ6D64LtZpRSEuytgZDOrsBS+hpKsULVAkvh9msn51DMBZVJkezOXerMMvEjcvjumeLl9Oys9Stb5iFIe0GLUkvFNbDbttQzyz5tHUn9MozBdPj+8eQ6LvpHdtGq1nxAZeKP82ceBeu/zbMzfzwZOrnKoI6NMiomI0UMtS7xfqT0r6eW0YR0paBsw/PLqhtTjEsWTyZ6ljRmcCtpJ1tcJLHB5MHz60WNSqXkbEwFiZ5OmEi/pIUQvXP5EzRys7pvCQN8T5q4NM9UbFI1MsWYpxBnQpElgHHkim7KVOWg0c9Hloqa70ueA6TC/ht4x/qJ/3CuRzDoSEyuuWR2rWCt0uKF/vHkejxyLotw2zdsM6pKulO6JX/nWeD9kYoaz5O83l4wU1pLkFcpjrowzzNDl4aSQF/H2m11rhtQ8Mgy2o8nug3QSe+J4MQN74P+46RsAk24vn5jHoMzPkDSHWVNsfudmIPSa9q6dDLGYF5XVgyqOLc0OVxgo1OnE1sjimWWi0/iVIQsda3siN0Zo+eMsyeORZ4TqQMrFyKTsEp5NVoN2lvDNmVYAgo4ktRgZadKoCXYdqM25ezG+NU4hiW2yfuepLJfFmsnCeuxLn3raugAa0bwqUtarnVZEri8PK2mwPLOreY6hGTsX+6tyKckK4LpA78u/Jo5WBQYzBw3dMeL4yUWUcHXMFlTS5VK1/VvrJN+sVs+JzChCzwmqSRxZ7GBMto+iq5ck0jP6MqG9DETSC3kvdNh6gRiUR7yVGUkBY+afzZf2xK3Z+8hD3hZpgI1YqQ/NMYy5K7oONEc08+I1YgZySIKkNlBc6bKnET0VHCNvGoQgzlHnngjIwi8d3x0wkd+7qI/x0afYVESmhEcnZQg9jN/VnMkpiuKQMVRRvbw5mcsg4B9jpzsTyaSKz5lXSlmojcju9+fKiTC83Oolj5ezzHeZ1Jh5DGfUrqwPyl68hkXtJplkp9ZrEAj8WQE5ZMSq2ua4ztklaVLVLOenHw9wsJ7fkr3NKV+VdO8sZXsArPELB6wYh5MoGgmimcPIc+zX1qctSV2AwN6wDFQcQpwzskMp2yZHL2/ZKn6NEWGwjC8G8MDKVnyQBK2fZvZjDmPdHCcQfmypwl1vGHnAHnglu3z4+vJ8fVE3gdczszJHOfogKLckNrYtkKrUA2cliZuz7cowxF1rA4kGuI152lMQp+gjtUKVCx2GAdxPJGtE2UQFXCFXhAK2tIdoSVpACxNq4jjdSQAeaVHV1Ou3jmuzL9UV+yZTRjt+SXXvcCczHGlE8gKkx9InPQTVAuyt58U9YFzPT+4zufS3Gq6R0wXhmUyIscHVxXmK4zHB94PYlZUjFryMxjrxCzthagBloFRPifykHT7tBc8hPGedrI5OxEtDQP3oBAZizeDcU3cBtMCudICJl9azhMlxxAxkoww/eIxduYZjO8JIKtbZua0L//C1Cf69Q8oDVqj/AXsrSHzNRf188F1OvZM8oUWxXVw6cTrDWTntpdUMLmg3qmelsC4KUMyxkLnma/rNZt5UvKeKXFSBDDj9r/9BS3K7faKSaGyMa6L+XzQtqAVKC1F+4zCmE73BHeX2lYKQiDxxMfF+OjM8xctY1brugpnefbZAs8Vm3IoUyi6BOKeguMsozNyPaYTi1CAr5NyMV7089SVJY7uk3GNNOoenSjpSpmnM8+ZH5YZSTlYu6BocmFqgqdUZtb4kfO3PKVZu/HavR2QWBrWuWxnS0aYhxO6gm6Sc5yq8owL11Xyplgh1Tyr1JVsniSMT1LqJkI1Y5jlAJ8AnzmKiXVPVEnEY3yOjrJqMMsOalrlwCVtdoqhMlcUxJIwfh51a3GGZtTF9GzXJw9HwQqmW3pGS/47nj/A55QDWQJ3BcKyCJJ8BmRmTRNWljhgeU+VpU0OvA9cFnHfsm7RtppMK8ApJYxJN5hjMlDGmWlgbV+X8s8Luvx5gpspISVvNtOZUvJ0c1/VWZ5WPrJ+QgQpqeeTPtEitFqZogmp8RQZyOqbiGRrPJ8Rzc91fY+lJnWi7humRqUuMDRQHep/dwqPlJ2G9BSoyGeVlq8lWDhQ+5+vv3+6ONv9C8wgjiDiJAvOSlAQPVEZbFooJn+Wq0cm/M4YXCNtTdu2oSqM6yPVQKFoCWq74TVDb398XIwfFz/+/pX+uDj+9p6evZc7/TGZD0ffXtDXCvqAOZBvA1Pl9lphS64rcRLnIK4smZgpL5TpqOQcMD41nefJvBLsJQXsZf1eIiHSpTGPgV9OvDtyZmKyhzLGwKRQtGE1KCWQkffu0SuuxrBKU+PL3ghRHghjPGBecJ2oVHBDQ7AjNZmzkrO3KJT9DfHBePAz2a20bCjYFpQ2GIcwxDB9wbRgW1bsXTMvUq5JJqwHZjfU7my3N0Rg+BMfnrv3gqTNnoNx3bIbPq0gopQ1pqkCpWxELcyZHWLdc1e7PiYxkoAQtsO2UV8nVR19zevF9fVTNeWLgg/qaQOM9zP1qG8b0QrzzNck5umiuRxphlRLhQ0C2z01YfNCpGBemccnNdBBhbJV1IM6L9q98PovX3h/H5zvg3n8wEenWF0b4xO6w3NCuxG39hMvUrcbtRVefr9RLCsixg79N9gmUZ3vX79zHCdxJjsoWbyOykAz8yJ/F6fdBdt+8c75/P5EEMqyT2lNk2lM/0kcGJ7c2jry4TFxVAtmG1MzwUmXJErqzIH9TH9nM+MsSUN///bkeg4+/njPnZcUD8eVRmihYKqpkimpLR0veepQK2imRqWLKe1VPqGM5JZqSWlfuGYjq6cwWVgsnLpORFKQHL7uOudIh/2I9LHZotL1SMtXWYGoBuqKRMFKDrTRLP0/9ch8ihVCF8U9KOXzChyLXL46n8y0YsnKMRnBfFxQhLCBtEq5vVEeQpETbbrUOjlzDI2MM+yDuXbzz3P3M3Y9Q7ZTJPFpDJal5leTZfj2rAgMQnSFS+U9Uz5fvPQ1FhsgHWmRp0iJBVNLsDPLYJ8NL1KEse7ZokIszavPrJTGdeZdVFuesnNi6/7KNQhPwUe+F80FTzb+lLnAA4L6WD0QhShMrznj7BcWaZgQq7gEEYOpgykzCwcPwiYhzuBaXKc9dd5X3uf9zH6clKB/HIzzYhyac9geya9a30GG81qKeIwkBf7K4vz+t++YGS+35O2U7Qvn8cHsx/qCg8sDHYFeedEuEkht6PbKVQ+md0rd1i58Yi6Uq9BKZa8bp8Blztf/++88/nhwfH1HCG5vW76hw8EVk5a7VYXaSj4ojVXeliy/hmXDYiRcanqgo+UDUjZA8VmZ12Se2eJWCeyekfeueYqPuZwK14X3ubCb2XXMri/EGURJeLAjDBPKLJnhu99BhZB3xpx8fx/0KIRXytT0OoqgmsVBSNA1G2zScz6m2qmtYlWzwTAn8z/fMyahQdn/jfL6L9Qfgj8VbtnE8W+eD5QK3if9vJjqhDqqL6jYz4bQtJzjhX66ZiITs0SSBxuZQYJMqBeulaFbikhq3lsFGDxzDj4nhlNvAlt+PiJZQmvPcrYiTDWmpT/UFgFBqhAtN9bZU5t6fZ2MKsjLBiuTRBFkBnGmkCJaVm2+VGOxfKrGlXNUUWRey5xeCS+MseHXB3IclGKINWbdCMi0brvo1lHJ/22WIcjGTCHMeCMiOI53+pnjnbaS587nD/ro9N5SrDIdJ4kdOTb07HdEY5ZYLqJfWJzX9U4xZWil2Z122+mXJbK+vDB17caSIxdE8X3D6o62HangkTMxJmyehADRYPjkcVw8Pg4ejwf+3pFzstUdMaHeM1vBx5GXbzVsyx19yW6orJEHluMbmdnIwDMrJfjznlnX+OW6UncbE2mC2IYUJ6Tj/ZmnSZUU9j/ec8F7smooQdSOqMNG0gPb8omMHuAAACAASURBVCcdS1yPYJ9BuJLz2Ktn4vLsV3JN1aBUpNr/0CkUHYidlK0grVBv2WyLa2R0vGyoBIOVpvbjBxKD0oRr3R2Da3UUE8IkJRO/0KCUJM1HKT97k8DqiGf2TbEL00mM1UVen5uZA5IjBQyfmp+DOEJHI2i1YZIGad0bum+UYegUxvxBzI5fPT+ndaLaJuyt0Frh6Q33wq2WnAxs17pPtuymK7BtaRv8xxMfM4kXari37O4zKNvOVl8/kT2MUNyhF2ga2LwoFsyboqWBGlEkvaQPTwfQMCRGiiZGgyi4KT2U93888N759scf9NG5+sVWhGqS4xsPBi9Jj6wFD6FfjvuJ+8GVJkX6vJao/hcW5xhJyxvaM5dyV9oPY7rhWjOpV0gHQkCoobWhpVFKQyVy0H1MYsbyHuYwfk64ZnC8nxzfD/w5kB7UtqVmdVdiDoR841uxtI0Vyda8ZyNqwSRTVyqO4AzxbNlLNiNEYt2+nRgdPPW3Wu85B7UARupHVYi254jieILv4DVHHeZgAyx1tVIKWlez6JQsbbMWIttF+cVks2slpbXUnIqVXJxz/tSbigRiPS1pt0Q4minzyOF8aMtusMy8rI1HxvrVTHybEYSMNVlI7SZFMjPTIhlAVpKsR6Bj/hyDfULSWomVW7LgYq65cUTCtr0PIkoG8djnqGnkaVsqqoVaU2hSt7YaczDnN+bo0I/V+Eo4ealGq7BXoc/CdNiK5UbbBhI5Xw5dTcXSwEp6I3smgxGW1RMJ2LbySttfshNPMK70iQ4bFAnUV/DxbotFVLKB1gPpa3HOxeeVKxdZFOZIKenjfDKOk3/8+z8Ys3P5yVaFVmDNdYhSk0dcW7rTej57fnWGFiZr3v+ri7O+fUkNZ4Fyu1H3HX8jGbPlZOiExX7VSXb0pDIoPGeweVAjePRrzbHy73WDfnauj87jH985v/8g7qAvmSSsRWi/CRINrjvNKq00vKT2cpxnhvHONU+UTszk74xTmJdSSsHUKNia5UEeuWORxFoqma7OuFJ5xFaJqtRbQ/od9t/XHTGtqLkZ5fBcQ5HhRFz4SHpBfdths2T9TDJfpSr2e0HODtuJmeR9rqbj5/n1IxEsBlYNe/mNdmu0vSVrpjvXeyL+tRbChFkSLBbPcylyhMoLRYxhiZXU+z1B0KNnU2w6VXeKGF0zjNdimclnLmY1Yyt3Ssukspwvk2X8EKZ2up1E78yR9y+z4PZSKbWybV/yXvWppDk6dgYynApoKfiXV0SVvWVSlxi01mi1UIlMq24FAo7WmM/J+e1iM6NaA1eCYLvnJj1tVS5XYM2pLdU78xzEfOI+ePrIscYVNFPsSPYy9kLVgqhweV7V2p7imvlp5Jgd52L6qrxQ/KMzr5OYD6wY9+2N2oxScpQUDtQ9TR73JCvOkd1zDWWUJGnE+zNjMH5lcVprCcy1PIkC1t2zUPRa89oVibAMjhIrmi1bhMmdmZnkvGbTyZy9JuPsjOPKk+HWkGboTNmZbZp5iVqoVilWcLVs0a/5JDNywcngs9mTg90UPWgkyJeQn77KdMOnEDz5O4tBoyA3S5ma5dBYtf2cy+VJmf+GRM5xWVpQH+mMz57DkoJJjnPQjLCAjJZXTZd9kM2G60hIstR8eLVuKXG0gvVBDJCliZVGnuwkEnT0TlnYTxOS3CAlj+9as1lGZAPK0+mRluc8pdeZmOUriomtLBwB+s8RQ2pGEiYtMqD3pXWN5SwptNa43bfFkB1cPZIGOD475fn6qAUthbqn7hh8DfvXDGsJT0QF2kb0ixkHISVDl5GkQdR1V9Yc30k4haBaPop5XKWHdcbFnI5fgS9nTMSGS24CGeKcz2opKSDIjlWyjufnK107VW5OHZZctOwtyfa15CntAbXkrLTM9Yxlj8Mjj4ikbq4YkF9ZnKU2kOBZJv08uf7rv1OkUaRgIyNmpOQDM6oy58xI+kPxpyYuUVnBE5NjnMwJ51WoHuwiXGqcVmmSDQYtghXlLuliSEvVuthrYDjTOh5JEnCcoVnKmYJvzjCBca3OLT/nsvlApt1N2fLELU4ZJV9nWdrXPoirE+fFXNYrvySbNZxIJKe0u2cSmWVnUM8ltChJKp8eCeWWDdsTejbGxZid48dgdIjD0FIobzWDWb1iA9BBf2ZgbWtGceUcLGgV+GWEV+ZxEtfJ7bff0VbROQgVpmV31//2jWIb1RpRL6YFZ+R89r5ld7p7Kltq25ju9NOZR5bM2/2eipgiHMM4o2EjsAve/ssX2mujlEKxQmv7MuN3jqPz/OjsM5s+YmBWqe0VU+NWapZ4x8ExB+/TeZyJpXT1dWf+jekds0fm1fx243zvqQrbNqCh7LlJz5Nyq+wvN9R2RBt0wedAn1+z2BThms5/Hifj8WR4MN42yla4RiqImt2Y1rnkg7Lv7OULUxpTCtEfyWetDTNlb7+hW6W83jAtFDFKWWln7SWfpfmRWvTjTzWXRlrqtLyg5fZrizNmSqOSdzPpo2cWhskK4FnDW9KpknI78tSQWC77NXr4jMwbMHtknqcm+1YsRytmutCW2fLXRWBDluolstQSlozQ10D7czwgOdfCPkUTP6fqn8IsRAqiJe+kn3viMvnmj+cQPeYKKZLlaFindJijsFriefJaKr7W+GUFC4Xiy/HA6uKLSp7WI1KMPkihfMtZXNFKIT2QnwRBVIiWkYlyeWpsI8dDtejPEywW2ylFCeR/95H3dttRzcSsz9uwajp0JmmUVssF2J9J3ptXngw2Rn4XE9zTNmeqlKq0baNte55SkaODOZxxjawmYtEFkc9D8tOXvkY+yQLqfXCN7Cz7nFyaCFDTRILKYkXFihqMuYT+Sz6XndAU4WdVpEsylyKC9JEmLG7Rp5AxsZlco/zsVoNM9adoRdUo24ZKRbFMyXYWpWFVVvYnjyg+IxA/fyE/36+qLErg+p7CsRXs9UuL8/j4wFRo2zJF985DJ66dbZyJcf5tR6JQJB94v93SxlSDZ4zUa/ZM/+1H2oeid2a7c7YvzK0R42LfC/tW2N7+CijX85l3wy3LhVKMOE6iO2cH6QHnBShad4JBjxMoVFH0d0FrEFeG6I53QbQh7Z52ryKEr2aDpMayn0LEZESyW13WcLwE9jEyp/H1nqyZ5sjpcMwcfi+X/OyBX+/ZjNkaFhvuFbPBsJ5MneuikzjN7a9C25W333fMK9YbZRfKRvJx5+SwhWn5I+PrI4zaClUKx9jo86R/P4Hzc50QciKzYxuUlzv19leuma6bqtCK8uXLRnfwM32hUqA/fvD8/s7Ug5DgGgOmEscSg++N9qLc7oW3399o240f//jONSbPjw/mcdC/fWOWArVm8JIU+vtH0tB54oPsOEfa3Z5+cV6D+fxBjMHRCiqFMlfwrFXGIzg44Uh3TV+cF2052zxDsEjPkDkYkQFKBq/cs4zc0lkuJpTh2HTGufCs/cDd6d2JmTNUq8b+dk+dM8pHLwmebtkoq+M1qSBz4qczLgg/UvwwZG0SjqpQvzT0KZmUdk2Yzn2rVP2fS4T+eUPo8/5Vc4ivrWBTkh9j8dPELNOx2bOTKob3SG8gnWDgR5Yi41rEPJUktT1PJGCrje22ZSZFq0RkkBGSeH5cspm0otF/5mdUcoeaiV0UMXTJ5ORTCeQjNZiihFiyUyMbLEnwWzt5BHGM5fK/iN5zXCEQU1M367kphK/GEiCbwsqNWRQh5knO3vbcUcUH6Mj7WgURS6qgyJIKGk0KpVZK2ynV0RKYp6wwejoiZEZK/nQFa5V0d0xXxuOZjF5fHVhZd7HWsGaUBj5svd/c1a9z0iMXvoZi0/GrQx9E+/S0QQxnXE8iKmLBDGNoSdvgWBvwnNmMOzrj6OhNaK2wF2gFxtRsLmne1fG86wlBscKsIC3N5bo1TLKU9/VdizizX9hn8K/EMiDkHbM4K9res2LwoI8nwWDORdAg4xnmzFqqEFjL6xdyS8j5GMkhGjmHjb4kHEt44g6ya4ppnkowYF4pIPHPcVZgi1UMuSBlOZaSe5R3XNWcs//S4nzZGlECXvIILqVgH5N4pvLHI/gI4HK28wMpFfE788Pp3zuzPAm5uL525uUMd6KAvBb8mPiPb9z3yut+4+Uvr2wvjRqNOQbHcxKWw2WfQlzkrwX1QlN3G5cjP67EipQcT0gRuijTJ+eZMYOhthwUgfUT7WcmbmmmacVw5t+zexYcrPqbOBWvmpJFDHvMHKW0gdaCvu3oTWBXuAr0wviRHlIthZzSPMEmUifslrK/WfPLPAaqlRuNtt/Zv3xB/ETmhTMyC/TbYJwDzsRB6i5YddruDL3hoZzPgzE6sz9RhHrtyNawlzv1Xtl3R0dlzpLVy5x8+56kvlM6FoUSBX8ccF3I/gZm2DWY/eJ6fqOMhsad6/WVkBvXdaFz4P3JvJzrPW1i17PzUpSXe+VtD7YdfDf6NMaVNAiGU8Qp6kgtlBJ0rdmF33fMYNe57sSyVDcH2hN3Y9uGlILdMruzjKAWT211P6DDY/wn0ztD/oLVyrbtjDHpz06rghWhvd2wVrnNjTGc92/fuK6EADACfz9yUc6MvXCF8m8FdcF6jufm1RNy7ZIoH2BbAhemrMyU1Zd4WfpvAj0D+Se2lH+6ON9+/z2H5i13j/lpgDXhPAZjwOXZKtboSAx4wPXR6R8n3g68dDoZgzb6hFBsNiIGoRe2Nep9Y7u90PYNPQaiwsvbzsS4TBGfzJG0d6mZGKVulLHlvM5I0WfLmn765LoGYwyeX1NJYmVfqPzc5ZVEbYQF83HiZ8+T3KC93TL/5JKc4634AWQSc6RrZEUneJ9rZhhILVglWa9kVS44WHZz53MxfYrBp9h+N6RKWnjnRZwP5vEgrueamQZxrU6jw5TAGfTL4RoMm4QYNgsSGzKzrJqcSbo/gg3WcJycXS6Z3/lxMiM9PmG+5IiB3DMqTywTrOkVZeRISlflcHSumuWdHyeRcGJEnXo3bC9oMUYYMoyYJbuhmy9xCdlBnhOlp80seSCIZ9jtpsqUz9myglWKCErgLVmxt7eKaf48lpiV2YV5wRi3NLK3JUJ4DEYfjMfFtYKZqBfmCX/zIMOStTLZkZWSPUn8dZTFXBqeZnGZP++55bah1pjjmZ30JfNQXZZF96zaBHRLh45YSjp/aXF++f33nzrPa1x8XM/MwVThGifnGVxXR3C0nYgbMoL+cXK9H/j9IurIqD6BMVL+JHPLZoGd2PaF9tJot1fa3vDjG6rw+rpzhRBT01A9rpSyaXpkzI0SyeiJAl6d2J1+9WwwPB9c58Xz65kYi/ueUYU2CMnFyQJbX8dJPC/kOLG9st1fkcvwiGQhzbHUMInqdCSzOchBtcwUmNffN7QJslvORmV1QXTg3eAqlFAsjNArFTx7ttwnI10dRzB+/GB8fNDebul77J5J1pFIxWDSz2AcQdQJWimzEqHE/I7jdMnyVMLpcyVxi6X7/14Jd86Pg+meU4Pq6NLESpPEoFgB25AxUJ0wF3dnOnIO+uap9T0/F2f6Ka0VypaLZ0QqymKVomVLAPXsgl4phMjI9ys1tFJycYbRymBKT5GIKVEME8VwejG0GfcvlVqFVgqXG89hXA+hA3Pcce/QUu3UH4npHB8Xl6f1j3pRpnA9Um1mv21JfDdSzXRmRs2Q9dmoYCObkQ45mpNCvd+o9xfmKcx+8nFkZVklT9QYk0+radlqUiW0r3HgLyzO+XHkqOQt+Hh+8B//8XdGpE/wGknjmxfgznUtAoKPvPvUSRoDDfVFJN83pBn1pRFXWrIMQ8agvD8oZ+fbmR2+TUqeSh8P5gg6Cld2X8fMgbFJSuiGldRkPhIZMRfr1kMRatb8c8K8mPMj49stiHECF0UN2ob9tWS46m3jM80sWkua+Lq4qyRmQ66kjV9km7/clLoL7WZgDabkfXc6/Rzk8yWLBp7RemqCthvFjHl5in6iMKQyWyXKF7ANe/sDu3XGdAynqOTJPtOaFTqSKliCwUYG3yarlfPkgTOk42wEhfosaxbHT7NwaDBt5B1PKiUcjU7vgjo027EW1BJYqWDG6ZN+OnrV7HBWQwuUPUdKwyvj8QR/R2pBq2F6o6iyVeHSznPmXDp8o9W0fxXLsGSsIrahbae2ibaJUZPA8L2nokyUao2ybTy/nzz+9kxW7pkZmhRoNZVsbk6oonIjqtANxntuNuO9gygb6XrRluMv3Sb+GMTptJap32VXIjKHNKYivVNKUHSgTTAtfPz4jg9YqyIVdOnyJo7s2o8raRK/tDjH84QtB9RHP/j+/f0nc9VHxmp/eiJn9KUUGvzMppHU1epypUurGVy7pTAg08JyRKLnhU6nzwT5VtNshDyv5b3LpkzM3MHBc7j/eaG+Um/qfeCj/8z0FBJ/mYeYE37BMia7jyxdJB0WZatYSyKcWob5UmoOzsVW4ygdK/QcB0y/sC0H/gkZTjlhTMEfeRqOsWRumuMlcaGQBnSzmq+l5/1qTGFGcpU8NiT2JBNa/jkJwTyyk7wg3oEiK4ZUpq0g1/SpxlwJX+5MT4+hswzPOfxLLywTZ/JJtjAygGksN0nRSilQ988uZMoSZYJ5CuAxRapmZs5Ml1COpU7MtkV6sDXnTPyJ60yfbGRIsppSNktRfSQ8W2pBuRYxw1IwMMeCdwMIoRveO9d7Z/YFq7YUyZillHHM9OYQhq/U5nk4fg3G+5EhWK8TC6VsmUFTzLBLsD6pZWIFtOXBElHxkYB0MzCZqSJDsyk1Fu+W5Z7ydcfsns/RT/LDLyzO//P/+L/QptS/bpzvneM/B6MNZl3qHM8X4T559DPDYqaxb5q/Xr5Qtjt9XDmjWiOo9nnfa8q2FfRWOLzTz87oZ8726gszLq54ZjIw/ARIxb6jquzacm43Oldko0MkMYjy7mgPXvcvqf/dBZcct5jsqG7o2mBqSVy/lYkUwULwUrD7jtSA4inzEqHYjXk5z/eEMwmBuWJReXyfnI+L7cjNoEvPof60nyoWoxPibDVT3Pw979hhTp8HPjrz48QfnWP+N3DlipOwQL7kJhC6MZtwvkjOIcO56iSAj2fCvKRtCT8rzuiT8+nEOCBOru1rrq9QrDZq29d4yaArzPSMqhnV9iW2cXw86d/fl8BcsL0t8FdGH9IhpDC3jVryumq3F5Q70yYucPx4Mswot8rHNfneBX+m6KO93GhbYbt/SSfRQkke3x8U7VgZ3N7uWKs4hd4H/89//IGEI5IVyvUclNZyslByZt72nRmTH9eTOCc8Rt7dxfNUHDMVaaq8Py+aC2/tTmnCpnCNwbwOvBquSuuZJPdWknGsL41+OfM8KNudqDvb64WcnfO5GnSlYC2lwRKCDEXl/ufh8r+6OL9/fKCX0XQwHs71HEnNm84iqCKyfGqezn77lHRVo9RKbS1b0T4zwFSTcUsRvGrO8koKmfPvyKbRZzf4T7YLi9nyKYL+DLANzOaa0+Wu+CkiVw1KzUXF8tQZy5WeJOVsUMl/H16zBscIqCGaY5rEH+UAPj7n1OTJ/0mLmCMBx/pMkcSwvJ+GJyaDT/6SJK9IVZOxs05Uj2D6lafeNRc1YHn+AsSNWM78kPRhSqRr35EUyvSsUooUPgUJ+LK9rdmg1GxXGJXPECQzpZRcmMSKIrQllAeEyZgZ8yCf/Bzyz4rl7+7rI13yOytC1Uw/D7mWHSwX8lmEPj5P5s/B/RIEBCn7HCMroaMTbabpWiWzXveKq/D4npUS/UjppidEWqstauISsIycZ/qYyBiMCGZ4Sgw90D1dJz4H5uWnwCWfEkfFGdhPQY1qUDRh5lYrs3f6GFk5mVFKS5bvWCf+Qqho07UB5h03S45fWJwf1VCc8e071zF4PDvmDZuV+lJzd5KCzMnuk1KUl/vG/XXj/mWn2B2hwpX5KRpB3YzXt23hKYKyNawW9ExocbMsPYYr4YXCng0gBLdB6GTOCx/CBUjJXdgtGFLS/x5Cu098rvTsUC43JII9FKmOtitFx56lD3yqVow464oViVT2LIG2aA6Qpw64HdmcsFdsL2CF0Ts+D+YjY7jHPBHbKft/SXXKBrJLhvre74gWzufMGebHBfMk+pNqRlGjviaCBZccKYxrfWWZ7HY3/4kqeVw3Zhfs+QMM7HUjroE8OqIXtJ7G6RDqy+9JY2dgpVBqsLWN7bZj1pHh+B6IRWp3EVpVTqn4sOT1lpoKFzIzM+Ma0/drrWVMfIEqmYczjmCOScyTa3SO68mYECPT5aQULpt0D57/+Z+IO/Z45NwwJpTs/saVuSxvbeOyytf3LRte3y+sNKzt1PbG7eWOlOyIX8+RtPWv2ScZAfPIX82WIGV7haLM6wOTwVRjSIoa9OaUAl0rU4xzHLg59y0Q25H6r/T5dz6eH9R5YOq81ldmc/hNcrMbeVC1287xddCfM2eD/6Rd+8+5tVaQWEGlatlKNl27au6uzHSMt61Rt8L+9kJp2QnzKy/bCU/mT6KbGLa4O6UpVnX5JrP1nAFQmTCd4mpdJ5Zld3b2pCRYnlAmC+KMrHuuJLFcJroi/ZgzTcwlubaiNe+BrAQqPjexZMX+tJux7lOsO+dyHegK003fosFmSU1w0kDrM6PZYyI+EFIgwdosPnXBn7szoj9ZqrGiEGwrWFnCBodxLTZSpi2lBBJLU7RPNKDu5MlKJEupT3TLoFbaqm6sLpFC5lZqOKUI+56nLZa2uyzas9IoVfEo9LGvHNLyEyUVSwUppv8d5SH1u+FrtPXsjLECUCOSnDhJ8FrT9N+SEj2/DmTmaGkd6/n5hDF7il7ant+MqTIlWUgZxjyW8GRmj4EEao/Lk5Qh6VEVX821Fimw2ZYFkobWkn0Oh3Hl6/QlYICc8Qf5fPp0xjk5n5PrWpY1U7ZYCnzNsZrkhTtf56f1zjo/QcP/q4vztt2yqbDc+VYyAqApaMth/3ycmMLrv36h3e+8/uWv+ONivB/0r9/x44S3L8l+2YMoEJHNk1ah3LOBcF4FH5Kdy+E83vvPD7lsSrkZY1g2Ea60QHGbZMsH3JU5jMtr1vi1AAOOK/+O80Q3oXzZoexQbsATolMih8LZtdRUq5yR8yzZUGmrEeH085HJ0XpHd6P+peE3w3dDvmZpNpeTY/RAmJg/MdkzIdkLXMag49oXHVBh25hhxFaIvcBmtLYlHEwl765fj2T+jEkpQrVKH4a7Y/OJMLn970nQfz5SlB/PmeOhvSLb0kLblqaA57X4R4N7Df7ye+F6GqMH8zGInh1cbcrLW6HsN2j1Z3Lb6Cc+Bv0RyITy6STaHLMN1Tv9+c48D77/xwdzTF7fcue7IN02Y2J7Q2+Cn4EP5/r4TsTMzm/daLdXMMOH0kcw42LXnaqwl4q2AS3B5OfjSbsJUgZTd9yV/hH45YzI6Mb2YtTXFEH43aEJGncIQ8cGalzaiPPEjyfDfDUlL2KNyFyUBxmg27//4ON88hid7eWgtMSiiAi+BWik93UqcRn+fDA/Dka7kqzxK4tzvxc8U8gpAU2hlBxD1AJqupozQi07FoY/z3Sw9HPFu01sXjAKPhXX7Ha6ZAy4HBPtQM9S8OwZjnqOQQYiOTPSWuMXeJeclS65HsC8OuM8uZ5PfCQ5PbfvBHmFTKJO3FKlIhM4B3KMZOxsmUSWtqblYFGybKsZjBMj79qMyJP5lmMXq0ucHvyMNpBWUkIXe1rTlASK9TObSFEYngG2Ullom0+y3+pwT5jXhYhwRYrT48iHQzQtaBHpf9RpWCxxRql5h2KiBnZTajVqUUpJZEtY4jN6hwS+p+a0D6dfqTeN0VM5NdPrGjPwK5gfgVhycpMolxsKk+Qrkeyi5CXM1ARPZ6nbGCxqPwKWR+70FFnoNdEZhMFEGaY4iva01eFCXwdNvH/PU/G8iBnU+x29HNOMvJ/nYCyjtG7p3xRplCrsu1GLUyz4/zh7syVJkiPL8jDLompm7h6JZaqbumce+gvm/3+nu2lqBgUgI8LdzVRlYZ4HFvOsF6CokoCkIMqM8LBFRYSF+d5zezGmQra07I6BO5lE9WPrtQ8RBqtDLs7UkDdOiXDi4SP8pQDiHOOBm3F/fAYL6rIzx2D0E8WoVaPX8nsX5/WlMptg5zJWOGgxpFowOpMitxuqiZL3KFfePzla49EOdIS8S/sBZObYSRLjkOlKd0HOQfLxZQq+f37Q5+TRY6ZUS7SEmM44FTsE8fJlJ8ONdpyc9zvHx8dqJkicjqIYI9hAu4W2diSkO9JP8tnCsH25YDkt0UCULpog1RQokwL9fYZgeQT6I98qaRNyJThCwwN4pYqcPe50uQLCFBg+0LY8rjbRVMgpRUqawiyCW4IuwTbqRIeQydE0oNBHh5yRlwKEo4OZkZHIvtwR5RrxiHzE6XpTyp7YSma7XkklM1KPRXM4WpSyh2Xu7JN+H8zHxNYpMcYF9TjR7GGM7wa1Qx6U6x4Oomtd+Zyh0LJzxPc+Jm3MWJxr41sM/WjSaCiTzBx/GHtbI54imAiHbBQDPU8sganTU1gtf/76Ef0CiflpeX2jNCA7ZnfmvdHF8JTZ/7RFGV8LWxLeqkTxtMHniHvubhkxoTsBCWOAj8hN8aheuoeJHEBzNPKGdg69h2xvyHqfTh8/sdb5/P4R9/k/lkjNS0YR2PZE07SIf79jcX57eaH3aFSMNulnh+0CRcmS4w9LdLDO+4F7x8fBwOMul4kFIjGf85kZIhy9RUigKTJXKbhnrNTIU/FJ8bF2bujHYHx0+qNFSff6B8iZLo6NzvHzO8fZeLQWjYqU1j3bUQ+Nk5MiO9L46gxqzsFuzUocwrbuUPp1r1AzGIPWe4CnLEeqGDFIbmekVw+b4fl7XlhzQusyNJvGxn+G8ECyQklxh7YJpzEeq7R2w3JwbuNOOjmOji3LWknKdY/P0maGEifhHKHC8jaxEciV3mr79wAAIABJREFUlDOXlxe2IuQSdikdg+Ixm+R6Q7dM/nZhTufzx4F/f8B9IK857tTFcTrt45P2bsyPgd+iqSu9kixjHotbtxzQcQFGp7cfHH3Qx0SYIXRvHcmZ7bLjKWNpY/a4v82nkSGXuK7MjI6JHYMmxikWzXiFecR4/3K7kpOy6zIfbMr0K+Zb+GJF2Wr5uuspTrPB7MpAGK1hwzhkURE9WEv7ZWdKYpDpR5ggai1IyowVu/j4scKfHkYSIV+U7fVKuhZ62phHp98tFEGXQpj+hboLW4Y50lcl8J9enLfLTs9Kbzs9NdQt0q9qIQ1Hl5HZHNp5hCql3yO6oKT46Ul5AqB9KkOgzc6cQutCmvFz5hYsl7xt8UVKJGOfR0R394/JaI/IL/n2hpfMkGjtH58fnMM4u7HlEvmhoVNY+ozl/TPIzVfJGwsIXVDnNSSOrn7IvUxCRyrAmGEETv7vFqcbs80wUPeG5Uu4aKJbg24rkW3EJmMNdF9jnLwg0xbp1fMQVJy8oiBmVqY4TkgSzaNtX5KzV4nEcJNFz4vT25044UYINdKWSS87RY2cDF3OlkzCRUM6uBfS7UL/eXDeH+jngdwb6fqyWEcOMhiPO/MxmY8ZWtUtkByhfImyWGsmqVHVGfdGH43eoI2YbavEwFo0UVLGijDrhvuJW8dW15yUo3E1EjoNa5Ppk+EBhUZXya8geiEpFJHYqEoieZjI7Qw8xZbj+7IU5IphM4TsaHCJx2Bq8HmrxvXgWivNo3knZ3gwq+bAXFZldKd9GN4MmpOLUDblct3It53DN7o2Sv0g14xuGR/hbMlV2Dbh43PtNL9nce7bzlYKNSm9NR4vD5orwyN1S21izcBirukWCwxZ9qlc4+S0MAdNTnwa7YyTwIbz2Sb3PinpQt4Lt2/fyKUidec8To7xjlwyOb3FiGMOxBP08PDNQ5gtvKTbHsE8UhLuB88Cxc0jTsJHmKVzjSCdVYbGAbW8fL7MsDoClpVWt3ifaDJ8RAdxcIb9ac+kFRI3m+NzhFxSEqJbWNPOxjxOOI7QnipxNxUJNIqthuSMDBKTSdeIvhCU8rqHwll20l64XK6c52TMgR9nzNha7EaXa8a3zPnnXyh157q/fN2np68egKzSjNVd7ZmSNm43pZUa/KC1oW2bUvLO23bjcRnI7aSLRcyjTqY8GCM2NW0nnjOp7pzDOM6O+UCTcX0rIV/sjmdlXsJ5LW7k1EllYi4YirD02hqeXt9WReJ8cXDzVkkJag072f1xwFnwBLdvlds18+6N3ifHzzvJYV8dZbYtjAD9pN0fzN7xupGy8vaaKFtGa8b6wdnf8ctEd6eUK5oyviX80fDjr6h1sp5cbq/srzf27UbSSi0fTIT8L98gCXnLmHaGNE7PeEvMj47/Y4TQf4Ap0QAAa4l0MGdJdeby0q3GiUJQ0ERxKeGkcOLh1WcMXgB7jRjU+wI3h33JoZ2I2hr4ZqImDlaN6EaqNTCCY4kKnNXKDiGBSCbnLdrhadl03HBbO9MMuRZqi4bg0cBQXawZi9HNcrM7/pyewHK+BNJxRgNn0Q1YroMYHazQlHX6BmczvKgRMRfd5yfz1/H4+59jJvfQXi7ObAz/I1fSJWFsEW+X0op/iEbTU8MrLFN+ElIKvuyWN3SJAtoQzCdJWph+17hDTMiaSVWQmulujHu8j5yFUhLbdmFKJztRNi+gs+HMp4hgejT6XJi2nP9L7Zdy0C6MELFbYnF+gpIvK4bitxLA1hww4bo9PwnguThzwNJyPGvzGa3RFXmNsCCX9XVGvnss7DUCnO4hzJjR9DEn5ukpzBUBKpuMfuIlnDySQoesmkkyUTeSRGMpFyXXErPv1dGdCnUvIcoQWWFZxnSnzzgQ5B8jhP4DwJcFGKnfW6giZoF+RggRjpMCfGxQW8dSYW4v2OPE7ye5VLQodj+j26axu/e5ck+ykrjiecOt0w/n178/EG3Y2Jnnnfb+QX7LbG8bijLzZGqoaWQOJIO8bgEHLmnJPp122ZhW6J+GzYnej+i6XgTvkVZmewvkyvGIXBF2EMjFkaRY2mMTOaFojQfhLUWGyyUxRUM189GxjzMcLxjt0QDBLjdkJuTI4AXPQcjLPhcdHeSoMKMlLyJIugYe9FJgnGH+lo7g7Dn0vkcLqZqdI77oJMwaO8ndC8mEsn7/q+5kzWRNnJcN81BquTutj2hIjM6+Fy6X7UvH/OPfOjaM661Ts1B3ow1HD6EsQcn0EtY5jyBlnQLJSO0TbyOA3bUgSeif4e7QegUtCDfs4wfzbz+w7YLVLe7i4sxjJYmNE6TgWkNzq/KldtMCqQjptl5DU/z9YP74zqk36BfaXemDmFQrtCQx2xyPKGX7JNcLeVfO7YamhPhkPjqP7wefH+/cf/xA/nhDXoL/pAxSV2ROfvkvF9DgM2vPjPvE2w9UhUuaqDnpDC6VLuicSAmr8ByUa0av/1gi9E8XJ2te91XuLQlaaMiDyxNJDetE0hSGazVEZuxCee1EcdjGYNuinNSkmJfYHWeP9n6LEN3RJDif64QVn3FnWXcLcftCI1otYWyumSfVSyVe43OkEiqD2P2ipbZO1hWUw0oFEwiIkypJQzQg7qQcd0KT+NRk5cPYGnNYM6YEFdCOANKoNNwS0n4LzfXFw7G5dvMRD1wEy674vecdmCWwnuGq8BSQqKNNRh/MPuKEfyoRJaRlX4KKNSR3C5tcZB8oWVPobiVMwD4nTzqCOKhHIwuBmhJ1ySyTgnhHRFHJTIu7nX/JEleV4k9YkCEeEsOxpIhPtjBrNGUzGMY2QSNvMo47Cz7TOixDDlg0KqB1/45eQaBZiyZGinv6nE5vM0roKazmSFAMsIiT7Iv/VCNe4hmAHCTF2Px8OrIMEAFPX5vDiM84b4Fh8BTV0ZyhZoqbUuwi+ny2VFCJMY1ZSDhTKb+fIWS60Wn8PFp48XSZTrXQm0RDII9ASGiHlCPxOQPJ2Tch74LPxBxBJ7CpEeeumVwrTYNGJ58d6T3yJT1Cj9C5XDEH4/gBHu6Q/FYRzatELvTtW+AZt8LoR8QpzIHOsDSJa8wyayJd4x7o3aKLa0K6vgCCH+GEmZ8P8q1QLxEgm5IgNXb1MWLzSBlMjaaDqTEcb8vMK4+440k/QqVyTlwr5D0aPUWx+wMM8kfwbevbBdVEykLSEFeEhDDRz/Bj2OXgEOf7Y5DaJJ0D2WW9ljALWz9D+5oS3U5+te+U0cljYm/f0G1nTxcEOL2tBpgxDuNhhrcWDJ0RC+Cthgk+v/7CefxE5ES3Gs2XGV5X+4iTXS8V0Vgoc56YH9h5RVAeZyBU8jHRksmjMUdjVI2bUnOqdFRDxC7CupKEsCGVSr2Vr3l4yrF55lHjmciF8wqikyGF8whkCjMMDaqJOa+M3vHjewgxdENLqIzk0RCHU6KKagP0uvP651eezpeU12TiMFCnvgZac46I+utjBtxNbZXxwrYXLClzCTSyKV0n05x6eyPnf7wE//nJCfHCbG0BqiuSXEIszoxtzcGIOZGOHqdSCrJYFsIvqU5/dlCV5amcESfOXFI8Qerqdi4ea9T66565/vHjWSrEa9GyBuIzMBFjDY+ZBKtHiRh3hNmCnhclzIppW3eo8TBkyRFTEvKewSLDxM/5VSlIVtJSIOnscVLpbyeeVo2PKwYp8RlpkN5xIhSJGC+FI14jEDgRVYiz0CkT7wYa2TABxZph+J4rdiKm3iHhI4QhIhKbqAf3Vt1Q9Yi4nyd9hCb2WSPG+TmBgUl8tqSYRfbhcE48n4x24hajh0lehL/Y/MDi/qgZ14Bul2inhvImS/yWGdrdeVfM5hoBRXltKSSZNgX3BGmPcU4O8n0W+Wpuij07naF6UgnGk4uukzwELE9h/zq8QoBRF7c4rQAnDM0r8NcFI6qKLEKS6NDq+t7dnNPjdNf1PSWLJiliiwoYgmyRRN73EC08OcsjiH9JNXow9jvnnFGxylcOius64i0GyKYBKjZSUDdMSH5nesZW46IidO2IOEdbO+xzYeZJlo5oxzbC6f6y4aJof5ZYRpZQ07Cgwu1HBzMuqqTspBppZ70FoOlhLbpgBsWDhztLXP77R6NeNnSv+MiYKeP4zuwn5493kiRu1wtlT9S3Sn8/GY8RAnWHckuklLHLDe8P9Phc9+ccUOyF6VCUMutSzhipBrpD3JnnIOctTspbuCfk8uyszciXmYIfDTsH/u0bnpTOGd3g44ESgLeQwEGSsEfla3ylbQySzRBa1ILWwrADbycPP0ia8LLH0pzG0EnXxsqEInugPz4fTjo62+M75/2DaR/0o9BZOlxAV8ikPz7DzqZX0ixctXJKYZIpJWNmfH7emV2wzxEL0BOaTiQNphQkJcxi1MN2CzdHjaZU1fBhuhp2ZMRWZ1dD8+3mTFdEBokZaEyRxY5SagErytw3TCOm0daGV/YBCGMmxlCaDUSgeqJWJW8KOqPx5GtTGhLvfSS6L9N6BIkisqE5s7++0efguL9HMtqjky4b1ITNI0r337M433+czDGJvGxD5KTUS+ArSIw+af2ILqp43EUs4d7CPqQXrBSkKbKofe4rTXqG60RdUdmYuYcG0eOC7L7MyCU9rx1rl3PmPMK4+vJG3Qtvv2x8fg5+/fWIINY+IwXM1uwSR/KKCOiT1h9MDrzlMEXbgdtEty38jW+vuAiff/9B+3mn30/QAEpt+Rrd4BmLrP18YG2Q0qBeKu6VJBEwW3RDZliEUk2ULWGt4WOQag5oWgnwmM+1GRpf961hDbOGjwtYQqzD7Og4QZ7i/TgWtIRLZN8rNo3P+4AppAn5UtlfdsIwB+M4mdaRFofPSIJ0ggW83AnTo/Tqx4ECPcFjNLoHQwoVqggiMEoscD8ctYGmR5wKEuJ+k6AniAmuJ5KctBEyRcoK/UygBXNBR5SNVInktLys4DOMFOBsWyJpoeYLmAf3tk96P0mporqRXzyaM/tSCHVdd+AVBIxjLczpa+r3xByvg8lQa5R0pdYtKBY+KVv7jbs7/SvZIMl63RrOKvMUnufhMXqZE9NJKZCrRgX1j9fmP1+cHz/OKH2eMCLt5G2n7MGFEVEe/cH0iUpBSMtfOBAbuFa81OVkkWDteJRjwYyF5BtKBAW5R0n5HGdoikbEEyJtGv9+ziOaKddKebnwhz/9gvk7f/v7nTkmsw3mEYN/zcGlJc+QYxFD5zEmnGnxRWdc2Lcrad8pv7xxPu7cf/3J8f0n/X6nvv6BvF+Q5+I0DzfCzwcmRlJDtxfQglqLROu6xTHUa+z8NdHeP5k4uWZSyQGZcgmcJmHTwsIIPKwx7YyrghjaR6iw5snKyYiHThQthVxjcY42ee8HMsCHkHNlf7kglvHpfH8/sD4oDCwn+mWLnEpjjb8Sw3uI2n8+UHNGMpoKPemSCUJRIYtw4lgX7NMIk9WdKcuLv7SoJZVV+kfTLmUhScC0bFO8Or3lxT17REOrLoFFesZehIJKMLZb3DO37YUxB4+PH/Q+GKOBXCBdyLeIsL+8voAJ7T1GZjYLah28Mx6RoxONwecdLlaM+IzFqS9spXKuhk/ZDuYgOum29NYeLhdRW37fFaH4XJxHw9SDZl8gV2GY/P6T05/fQxKGKH0q+3D2PmkYU4Pjg2hAkaTwmi6MpLQURPXxOSkupFy4/PmPDIto+SRQOkvoN0klhXLEAmEy1OMkuNRAZGil+Qje7RbYjeQlWD4/D/w+SCNxyTualDN3zKDkIAKc40EbJ31MRnPm2VduSeLy8oKWcKRoFtp50M8H83Egu5IuVzwrI03eH+/oUNR+YOPB2Bv+tH5tobsVq7gILQk6jWINtAQlL8WGkWsh50wpMZ+cunbzZ9cUSOkKpSz6+0RvgkgFeYuNT+q62wpFJ1nCRZMU9gJp39j2F9Ifb/jLBT4eWOt0OzEGJenK/6yUS0QO9taZs5F7CDbsreBzcLTOFMFSRvpA28AvimVhDEJ8vr2EmL8mRu8cfTA0CAgxJ7fo0qtSayLlSs6FYZk5Y1ZIdfJtj4bQkiZ6EpSOe6N2o7hSr1sISfqBnQ/az39j4ugCUqtlalc2FbaWsDl4HN+BRNI9dGOSOXdh1tUgbsZ5b5Gm3Yyzd47mfA4ovz64eBwS7QyzesnXIE6mEylRPaY1XurnndGj4pztpJ8PPFYl3gM0d34czPE7LWNPtXtaHexpEk2WuZoqGj7KUDlF2nHNJWL1asWPT2ycZAnvYb7U5e/sJDPSnCsQwb+E7NkXSlCh5ETNG7Xs1HxBx0kfnVr2QGV66CX7o2FtoBNyyhEMC0x3clq4Dh/BeHHBuzMOI1WHQsCcav4auczZg3U7B+yC1BIjIHHO3oKbmizGP9l+c80vwrjrYvVERgVOX/re35oTScPfmXTZuFRhxh0yAqE8IhSShgNHA4AcjOWEScYkE2o2J9uMJt2aPpQMacvklwuyb3gpwB33wfTFC0pPnUim1EK9FMzDqpckUDKyKT5WSvOz3jOD7vge/27OaG5pDkQMJVwvw0bMpEUYawwnT+9tTnFlqYqcINNXMSARzSd8Rf9ZitYaGNmFYikkcSlBa/g8Ge2O54JuO2LLumaKDkFaiBq8H6A1Fucz6CrLIijEOGecBtPIwxg+aDYZ4yBplK0J4r1opmpZ382M+MdEBCxPpY074BxnwpbTSlIJF4/xRYUY7XcyhK7eY16XFRnOHDDaoJmSLjfq5YXXFOOFR3MkO/N6MmwwekPbJ9Ian1KZLsx2J0qaNQMt0b1UIOdID36KG6ZtFAr73KnXC9vbhfQJ/RDaNmnp5LDGcRg/vwfXtjOxucrrtRAYce9Ui80kbUqaF7LtX/F5vijxqoTqRQe+AV4jQfnngZQ9Zri5IpMQgE/wvmHPWdf7BHuQpcajNDvDJr03ctkoNUpUnZNUW8zKii7lyoZLiBOyRGBTzhrJYRdWxzkMxdY6LgPRycvbK5d9o7UgHTDiAfr2xxtDEo07/ccD/amkRU1IeQ+czKtQ6pXb9S1Otc+Jf//AHu+k15eAPVuGBSFrw/A+Vyc3OEWaE+mjgQte8kpBi8Zte8ygEpYQfuAWVjZZo6ExYQSBLi3jtyhsEne23ks0jFDytZCvyu31Fy4pkKGCQ3XmTNjthZQyW36SAx98orx3yP/rI8Yzt8B12k2Zn4N578zhK7/GQnfrLZqdNQJ+kS1oilp49AgA/vXHIxhN/3UPMUQtMas1CbRM79j5CO21W/Q15o6UgmxrlJYTXrbVWf4di1Ofn5asVrnLKrEnSTOpJPJ+i3TfeUS0fHbsnMwWg2Yxp7uHem6MuDuVNU7JwXd1B5EQD0T8OStU9jkgXwNt8wARL/OqyRpit4H8lqsQ997FuIkZvcSdN3SGwX1ZQ2wVCVGAxV3GxZd8jhhvfOV6rp+9fnWzpXeIjqBbnMg+x9dg+bemQQCxNWA/4RtcjZ8glEUTAfgSFIiszwew8tv97albfqoHS0qUUpjdCCVunE6pBK3QesNWhLxfMiQl5xIco0I0YvDwq47GPBuztbh/epxAglJKxWXSXBfnKYboKSWSzJXRsrqjHq9QiM9XNU4nd74+92h+rS/fJxIIt5hVzfDMis0w5nsQHJBMzjslX2KMZyOIDQqyArByCm+rLAbVMA9+r3jY6jw+l9EjcMnnilpYwUuWgz9l69lX1ZXLqgwPGWYbDVVZntX4vM3j5JUoMeM09giswsOrKx4UeHuqE9Hfvvf/7OKk+vqsCuahGTwFZjL+uwovqZL//D845uCU/8mUyfF0JPz1YOSM68YxQ1N7KXc0C7Lt5FzYt8uXE+RswYJpn+/4aEh7p22Vz28P2vuVc1xjZ28D8xFhSSI4CZtBAq855FpzSuyeRJqzq2OpoJz4x/mkpaASkeP9fsfFY/EUwV/CBOtbAK70ZjCj02lrRuM68CdO0CWi1Y+Od6PTVmxCxYsyy4b6BuNCyiu8ZjU45tlBhZFHzElzJK95EvaXjZQyLcfs9vi8R8LV9ULF2cVJtQa+8ziQblxedlAYMrF+0n98sl022Dds35BaeM0Nd6dXoQ/n+/e/Ysed+fjgnD14vE1JU7k+GrlmLn/+hYfHJqSzo7NzvYWY3Q4Nf6gH0bwiXLeImbBasJywcwOcXFs4Y46JLbuZ5AapM3TDXbCfa3PaBsJEfWB2wcZO6ZWrF5xO98Hf5kk3o/qVosE6GsRCyh+DPJztpYAKzQvzwxl/PZnnndEecInPulvQ21MNrbOYr0BjIS+Q+f0j09rkZJIkEq1L1zX2GvHPUoCN9MJw5xgnWCePB9o39LhwygU/K8PCt/r7FmdWnn3lEA5E+9lnsFWGRWDksIGNaIyYRbliNn87VQg5n+QUsz0idZoV6ycWJ4KIfrFo1QzNiZljNkjr0Dp+RjvaxEMJY2snThL5Bx4C6NjKgeXASIS4u5Qdq7YCbmVpS5cETmOIbH0syRghWk7p2UDm+WFIKYGdmNGhxmMm7MZXP15Uvjg7aqDTvkh1cbLH6RSH8Tot9CkLJD6PlPg6dmwZ4CT+G8RuPdfr1yRROilLTK5IzphmhihyDHTAZQ/I7TRnWg9T9wjUp+doZsXf94x5jO9HJR6YjJPU2Z5joPWZP5tTsclENqkvn6zb0kOPFSK7GEr+76SVtuSK9Bl0jUtsoy5hT7Ojc/qdI02GPRjead6YQ9BUV+ixrtPQ0fXadQuC4GwheRyPhllfz2XMl30ZxQVZlRFf5glbaNcxR4Tg1jAg6ApOEgD1wPbMp/owQnTV1338KzIyUrf7ENpYQVq/a3FewlenNkk6KWLhSjmFz8dB88D0Nzp9fF9Coo1+drr2KAZtknKLXWi7kcnsviFoeD2HhQlWE5KEsl3ADD0NasVeXjnPxvE48M8DP4O9Y0LIyIBUV4m2OSYnRsdnlDDTTwShTEXSBV4rZzk5yhEezDnQI6FGZJZYjDBSka8EKks7ETTnQahPgt52rIN56CoZQgr77ldZltLKLe1GnkbqJynHBhcNIQGNgGJjRrJ0LsutE5tPIuFNYiw0YvCvMzxmIxXaaZg1UjZSEfT1hrshnwepFOpLZajSRMl/+aBM47/83/8DrZX262D2D86Pvwcuo2z4/oKUjdQ/yPMRYvSUmEuzXHxwSY1NJ5fLCy6F2U7G2bjW6ETPrEiBpCVKxzHp7YwB/rHKbtLCRcoareavCk0eRsnK9qcXJonDM+P4oL9/5+/1g7sKRzuZ7liJRtR23X/Do1oYBjY1kir6GuOW9vE3+uNB+/xAS1mz4cjY9HPG9eqVmBacz8zXRj8OvDnnw5ku1Lc/RLp7UTKT5NG51kuiHx7Iz3HAdC6txO6cc+iwT6MdB91P7r2Fo+d3Lc4Wvsvewhb0nDX5cN5HR00QPzCMkVdtnhKaQxHjoYpfdIKQLMXtdaAaLe/07NKmEAXHJMHRYpALJimaSTPmZaHaKnFSrCj0ITPIe1PiNRIk7Th9Fl3eCmLOlkIfHP9fzJ68TgqNVrl04N9FuEuKUxdbFIMvg3PYtcQy2Iozj0MABLI+ZYdGmkJKTtoTuRbKFqons2jk+FhVR8pBCrSQdpnLiqSIGSdCqHgsTof0lMmlAqr0Hqd3686YtoTiYSh3GibOOZ3UJ3x8wONYnKC4Z2lyUnKKFBIp3HSi+DmZNpm9cRZjJmf8+gA7GPc7ZoZddzxpSOBWN9IOw0fkiaRsuHWiqsiLAZWYY8Y81E7cBJPIPG3DVuEWlr2y5ZDdyUpMX0FIKSVSCWmpncY8Vyc0Jo/w484YkY1qPuFaQzCvivpcJWxkn0SFtO7oFgommw/mOPHlJU21hH85SbBwzyMEHq7MFmOlgJkCVRGt8dyE2DSkgEC+g/zeUYo/Dmw450fHk0LWSGkag49m2ARafPH6WsiycJGr7LEeHaz85JjqyqyQHqRwUUpSqjie40PvM/SsSSeuGSOvQCMPM0kiMjRYJ5Y4vcwwdA8NWBRxLzWDlKLjZz3KspqMA1sp0MpC0CNLL8n0aL3nhOm2dndIff2d2bHsdB2rYfXAbQPb+G0TjNMha5RabBHUmuakXBJlqwF4Eqd9HnENOELkb7kgsyMWNr1hMH5+MHvD9jgZPGs0zExI18ymmZ4qJonzAJsemEYbMEeU/CYgJ6bOMSc6wX/9NTLnZ8LyZCRjW9SEum1xzzqWrO0YEXp8nPQtxiXpCEnaeP8IA3V9CZmnE5a8s2NHPAd6iwrB21qcUog48MxowTTS0RA3Zgq3R+sT0SAo5AzsGzoUM0FSjUU7ThKZXBP+mMz7ZB6T2QftEoZ2+8tPxmic845smfR2ic6wEd3jOYL2L8KkRmntinvF58bsZyTklRi91FoiZzN5XO/uD578FBvx/jWHs8kvcbXQ+vIMwEZyiPszgv4TTsk/XZzddwaTJo5q5EfIVhAMvZ9It4gr10R9uS2RemRg0FrI2FKwYjVlquy4wMjR7a2mXMqVTROnT6Yb1lrEOuhCnVyfu5fSNWZIPuPkmDLpHmwjTSUCa+aOzoSPe4TEaIWnAF2iGch0aHzZjtC+LFUCPcpQbAZfI0WHs1t09nTMtZuvKL/bjp+6xNMRb572Qs5KqXGPxjOlTOqu5K2Sa+W6O8kn5z1mjk+52OwtPIUOZzsY8zchuC+FFOeIbMqSME2crpzvD2wYMj4j3/TzE5IjFVLNaInsUsf5/pfvYa39eGAq+CVM6uqhnU5PqqFN7p+f8XObgM04+caGlEq5n+gYUIIr9TBHfCLN437XBqMbNp3achAl8o7mzLZfY4apwvxh9H5Gt9NDiqgpbIBP8oGm2FzHEqKEOwpKXbk3Hz2Ie4/OtBm3xyUvtBSzXTchS+YlrfGXRzpZUmW7xbXl7hoVi0zKVdhumXF7ZbaNPuJWqu7QBn107OjYJ/FsafQ84jlbtsjQJlBT+iLYZ1ckJ9K3X2LJEiINAAAgAElEQVQj+D2Lc1AYrgzppBRyKYnKguSGpxEOFc1sl0t0dm2GdrE7WmOQrRru8+xbaDnzCFWlCyVtlLwz5j0Mtk+/nqb4c7swu5LKcsLPhB8WdjGCKzPHADI5RbgMpiTrqzTMS3+6dighhvwj5p4gi25g0VBi/d4VOEMNkPOwFUVufWkzA42StjipfESYr3gib6G8ifulwEhBFNh+y+fcarThfcVZJFYza/blBXTaSnK2NdZ4elUZE0nhQ5zLh9ruR6ieWrTvj48WlPmXRCFFVmhNiBif3z+R7uTjRLYMdYuxx9f/ZDX3Ju04GG3i9wVt0xmnxMxw9Fic1x3PSncPMUGPLNbRZ5AuDMqyoGmt5JLZrzXGQ+o8PsNF4mvkkDTHtefZNETC4aRK82jKDMJWlnOAxu14bgbRj/DVBQ0CwZMuETazXUt0B4QwW+fMlqMhNu/xmodM6kXZviXydmW2HXn0xWgKLfGcHT8mfq6Dc6UCiCwhisShIARVZBphQk/B/tXrNTb/37M4bXZEjOst4aVg+06ek61PhiQsg7yBFmV7jVq7HYEesWMgpaxZnQQKoyV0z2xvbySZFCZtCOcc9LMxZ4/yEudkoN7R1lGE13phzy8Mg7/PvzPl5PQWQ3s7EVJg+Wdn2mC77XGKly1GIDbAwGaAt8rNQralHqSCYcsCZPhFAon4/o73jNdEylvYzC5bhP7MUDtxKP3jwXx/IBL3mE0vqCi9dXx05kOw14zuBWkT7Q2+bYvQV3Gb5AFDoa1zXsmLJ3QyNU4J5IkrSQxCZH/87QPr3/HHB+JOub1gWZgU0q7oSwapmOTVuJ7M9ghXxdXJF7i8wujKOCHLuqn1iQxj04pWj3vmbHhvyHigvTPU0E2wy4apwP1YhBFBPeL0Zo4mD5Lj1xFt/9YjmuNaE4/0QhOhr3iCrSiSnMfnBzId7Y6+FvS1hMLJJn6M2HRvgRz56OHddAepG6UUUorZdd4r4oVXL3GduCjJQKZwqRtb2bleFMNo8xHPb8/s1xu3b288PCIknMakh7DAQzjhJcMvbyEHEPBl7zsXwzf1A0UxjUro/vM7uUW3Vz5PnkT4//TifLb8SYKlNSa2YAG7p/jvlTglkmOEgUV5zh2WOHsNhUUi5zLVGgJpc8aAbvb1hp/G8C8UST/JM5NWyYWG/9F9Oco9pG2rAx4tGfGvE8rzaqOvw9Bd0dzQmvDqcUqea0i8DlfNhTktdsmzR8f5uqRpGrAyusedtxPm7WHhzWSFAIkybEbswnTMl7UuFBZxNkqcV6Fmeu64z9pb1w9fOEgW63UpFFwUc6UdnXkcpNmD/qAeC6Xqyplc4yZfp4g/S3yHIkiJTBOfBMKU8On6eu3icTeWKtDT+pwc5gwQdtb4FfAjTOJoIilh2SIvv6esTTIG9WOGRrasZptKxGc4Ql6EvRYPR1RKlxB8uK14D//N0eFE5MNTcCKaVuRG0LMkRzVQqIvet2SOEuL7pEouBXMnpSPm1imTU2TCqLe4wq2rUfg2F085hqFLpw0mBUNpX+IKX0qhSbdB6yemoRmgP36jR/xnF+fb7RvDB5/zg9kH9vEzFBfujLJD2rmlFEakjxCz1yz4tdDswvSMHwp5ItWxf5nIthRCD8g/oOGhr90EkrJpxnGOobTHyfGvP6OM6A71yTqdSEmU8Q1jYuWTREFdqPslZnKXiSb/eg41hwvGksAsDDYsrcTt6YFEmQeOoqliIwadqd+hP9B8RRzaT8PHhB+/hjOkxkfoL5c1DlK8bvFnaUgytouRSgG/IMNIc3B8P2PT+xFpYiMBpXC5XYI3Y6AXDUbS2eNUTEtlMgODMe3E2sk8TsrrKylHCS9AyUYtcCmJtODetl3xtNHyBuYUOqUGnkOJkiwKTKGNzOjG8XmHolzeboxcaWyoTCRN/KVgRfHxwMdgfNxJJVNedvYNLkn5OAMs1rvHxnQe2BQsnWx5Y+dCygO2qFxw2G4ewVFTGafTpoVn8rNiSzGWL4KqU7JEOcrAhsYGqBPXQVo/xz8EH4INgaE00yUjjHkpNsnjJWbu4ydq4LnQz8nPv03u//Ov9L/+yvW/vbJdNkYNtpSd/YvCkK4RZdkendEn9nnECSzQZ+P+8f8xzgfDT4RbNBDvJ8x/jN/7p4uzlBQt/mfA6NFj9170O1mXbhDmnLFbZol505bQvrCYPjAzxmyhinko8zHhOGkpyNeRfr12YHP6MWhH57ifcfJYDOFJc8HcBSXHLqiFpIWUMuumEd3auYKKVClPCoHCI0fWh6YYQEtNcVK3wE6wJGuqhax70AC1xhf/iHwQzgE5IZJ/I7OtYCGQOBV96WYziJTwtC5r0nxG35nHXdMjNOmpXA+BQnTErY9VeVRUlJTXCRIdlGgUZQ25oT15QEGHqDlOhmSJURO2gqIwJy1inw2LMCJbfGFfCowljVNdAbQziBJk1t8b35eYhVcXwnpXA7eiWdG+mEa6XCljLuE3mOWQxEXCcJzQ6Sl6WdK/4ctn6dEB9iXnXM021bK2k0CMBp9pwhBmfwpcFidohVyJKLqsCIZjFkwm3LAepzcas2U/He8RQhxSviDsufkigghFV+ByKdAUNaOlIGSMpRb3ueSA66qny23zTzQI/3xxbpeEfTb63+601jnbSb9dyJeNq0+KR7k1XDinUhEuquStUJJjh2IDZm9x8v7tbyFonkqeg200zsuFvu9c+o1CRm0wR+P7r98528Hn53uUTUmiE0jh7Vv44Uq5xkwq39iuhdvbhreO98H73yKMV69C3Qq3W4wGWC6W0+IuLlV45MqYmfGxhQLp/URNqbXy8vZn9uvOz7Nztsb8t18D15kFHZlkW+iiKwGb9nBkwHKVSIH0gg4jHREKREkrd2NCGnGPmgcyKslCfOAizI8T64Pz1xMRpf7pT+hlZ3t7wXpoYG1bD/PVETXs188gTmomXQtvl0qpF1JRHjMzTChloS2PGsnX7w/cC+IVKzHSqnkjq3PeHmgOQrm7UKdy5jh9ugdGZV/sYik7ei2kP1wQqUEDuH+QRoOaMXf6+ICFuGle+DmV87Mxf7xT3i6kLTM8oR4oTRKMnNDeoiHjE8PJLy+krVLLG1OMkwSzMceB9wdgjJ8xTbj84Q9oLeRtj81P5koAj5FabxPaHR9GfwdSIt0iZ0NtkJMgt4zuG7pf0FFCXliNbIMyWzTVdGdLlyi/X/+NPg/64zt9gDVIZSf9kilFSUmYNccs9fcszvn+wM+B2k5RRapHKMyew+AtjvQGECWFC/2YTHfEcihWCnQL4sCwFQQzeiAdNdHdaaMj86Cb0ntjjs5xdvo0THVxWeKOKT4ZJjATaTWbmE7y8OlZIjCXrgxTLijJdUWbh6862VImLkNsnHagMzAgzNC7ahHQWDz2+Ykd5/LvOXrLsbNjLIwSq00Y3dRlRxK1CPd1Q31GlIArPFoIJQBXxSS6kz75uksrARertxWBl4TB5BwHvXestRBJlJhx4hobhAhl2ynbRq51dY6FNOI0YKVeB3qSWJgSQPBDQuHiRAMta0Z0jYAtGm7dRgT3rM76/FJ5xnlk3enjxMdJf4RdirKGfCyhvSmzCedHjzzTR0det3g9M040JTg9RVO4P1Zw3HTiXjw753EwMYbFphyGiLCyWY/XJ8T9EtHoT2iYI9R1AbSd0yfmxulxGqasXC6F2+3Cw15p2TEi2CsTSdVvf9ioquxZONvk7JPjcTDMojHliSQV10FRxxbzNusa7j1Z0L9ncfa//GBaovBKqZ1UMvO1MK8liAPTmMex0Bo7Q+B+GlMLqhV9U2SD2daF2AEbMBozZTxvnG603jjvDZrD/YGZ0dxXNH35TXu5uo3nSEyBfcuhWW2NNBNZbissxume6e58I1NJzDM6RqkbyZyaoKNMW2qQ2UjtE3pnzBE64EvC9R4P5N/+jXE/kauhe0L/FJCsPAfWgl4vo6AeBHJZHsSUhJIbKU1SnpjV8MX+PGJssm14SpjukR42UjgZhrHvlbxltreKA713Bp3jeITz55x4zch+ZXhBHRLhFNlf3thuG/myk1M4Q8pHQ5qTX2/RnBmCzL4qiITWzOPHyf0YbGOSXCiphpDenDY7R3/Q52DOQVpolXFbUWmScVP63ejvn/j7B3M1wkplzSwLIJgl+qfgHwfj7w/4+UD+fEUSkcE5wqOqk+D1ZgmSyREWRWj46Lz/DJjbaINUnbI7LYUPdPwAnVHeqyxpFRa6X18ozIVS+eSTwaA7VFH2i/Ltjxf+z3/5xvcNPn/c+OvjpB/OVuG6Ff6v//Zfud6uvH1746//+q/8/S9/4f7jLzzmA5s38ESRK6odkkdytubVGvPImPndixNHtsQf/49vaDZyfuXdJ3eM0jvYYEoNXmpKazE50wZ9DtKhSNPlIrGvgCGfGyYa0P0Z+BAbK1BWRrhILD9nCog5MtfMUISgwacoYach/aA/EvcfhdHvzHFiZ4dui5c66MedIsKehNOFs2SmE4IJs+hC6yWkgSmimHwKg5AIypbIktFdYIsun2jc89wiSCilvO5rTyF9qKeSCKortLctEJXkENnnEneSccYmIXH3lJSDTrApaStxRxstXDw97oqpKMMjASznHmE6rzFHrK8ZLUo/A+LNmNx/vTP6DMF2SnAO1Iw8hZIrKe0wAkJl3kOGmHJ8ZweMx+Tx3li948UkhnEG6V6mo54pM8juUyVyU1FKj1my0AGNk9PiLtfFaEW41kQtGS+XuHqfbVUjQbIjpRDClFVxGHiLhO4yBrlulHIhZWcmY+zhotYSEsw+bb2/B8ltTQs0jNYS0LigIVqwkAZoq/T3zv3XTx7zDNu8d9KH8fH//KT8IbPVjV/SL9Q35cdf33nIEXGNFpWXCpRywYvgRf8diznew+9anEOcbVP+8N/fyElI6sz3O+f9IKYQwgNhqgOCF8cvzjwbfXT8iDtYTAZWnsoU5oo5GO4LYTmwGUPaqdEw4OkN1Gi964gGxHNxOoq3oBV4P4IFI0LvZ0jdzljQc8RIZN4HJSfkWmlAKxk/l4zPV8Kz7IhaKIbwuC9b7K5aU0Sf14SUEG77Iq17ixQzWxBqZKzFGa9X1whJUlolrzFZQb25xN3zsGhPpKU+Spm8C/Ui5K1gwzk+ljKqWzQUigaRYEx0sVLTyzW0u7eCoLRuzM8TOxofv/6k98FWIulaSSv5OzCgSS8wPrHDce2YAhtRqp8wHoPjs1M0kVM0plSF1k/cIFsIA8qMqmVqOE1ivrkcItoxEuYVsUhEa2L0EqXkVhO+77EJPeIBd8tAjZFFygih87UZAAD1iYxJ8UzNb3jteBo89iMMAyWepXkaNjp2PFCZKDMmCq6L1u8RoqQLVDYcOTP9o/P48cFDO0OdrIPkk4//98KL36h/LOT0jdeXnf9V/jff5QccHZuQNo8GUNmgArtETsu0EJ6Y/IPV9x+Zrd9eydedl2timHN05/z5yflvv6IpZE9yyWEg7R1z43gY/b3Rftwpry+UfYdbxpOQPcq/1lLIwGYLU65kPKU1U41mhR2Lxu4Rz6dKDG5ThKEmUa4z/HfpD4Xh0Kwz7o326FHv54RuNcYEHtiQH965H43z0Vnj0ZUgNnj8/Ij7arRKQSakDdHM9eUNBQ5dXtIanVG5d+T7ifx9oC9XZCv47RLvR+O+wzgjTm4s90tybE9h0Vp0Ar3mpcJSaoKSHKdwdGXej9DZSsbzkgSmOF13jY7vfosFOyi4LrXUcB5n4/5xcLwv0Xdx5uMj1E1WySRMC/39wUc3fn7/lcfHB/wpmkjYPe7ph5DFefvDTinliwAPzviM+1oqEc+xXy6kVMj5QmIwxdhSzMUnik1hNGWYRYxjzcCO1kIqics1gTnHI9H75HF8hkj/e6iwUEH3kPYViQH/FKHbwI4PLkWpJRQ45pBGxaeSHlHthaEgY15XMLMRYdTCUTXyP++Nef8bP//14MfPv3A/fuK/XNCa6PL/t/cuS5IcSZfep3Zx98isC9DTFA4pwvd/K4pwwfmnp6fRqKrMCHe7qHJxLBL/ZnpRG/YCjgVEIKiszEg3MzXVc77TeWvB//3+G99a4xzv/Lr/yuf6hfy7c7zB78tn2qfUQLsbO8ZuRsuFmYJa9jUD/onFSS1Q5HTvEbQ+6Y+L8XZnvrxgWxXQKsVyf7tSjc+JPwa8SiM7Ny3O1OTfi6VrBLWjw+TzDCClQkyXM+G5Ruy5OJMUR1mjkBqSb+XXTPRnxLwzR1CXty+tRZ+r02NyRqc1aTDL8luOqZDU1i6NBkqGRVJb6mhq2ckkhp3MFIqW94Ar4NHhfsGhhDNyUcjtCjZS7IOaKirNWYN7SQXV+dNnkLKtSPmghzG70e7yE9q2tJMBpKoRUglSBHUTE6i3tHSlMMNpffA4G++PRj1cg/IpVEq4yuiUM34qf/R6PBjtwUwHFIjWSSNIPUsJdlTqvlE2GRTCZcuyQDDtTcBticcLpEs4yFw+VJTe1b20JZogp/WzqdLY1jill8SYkxldaJZHJ0pVeVtMyWzIKznTKhd7g6gq8WuVSH5mRQ/2uTyeEqO45xUsteInMqIgAFdzvL1zPk7O8UaPRi03cjV8qAH1dr+DDY7asBejHhvxcMoloPc0bQL6nCTgqb70uWbcspjKP7U4v337O29vxrfvfyOaMR+J87zTxkVJcvknk6n0vNoyWE+okH894K8F/5woPRENMVumS5aXgroVgoGHM9uS+NWb2vw0mCKw20iIoKRTAb+UnckOlqn2mZgn/piM+op/NbYalGxsNUMYp1V6mzzeHyLrjZOZ9eevZngk0stNes2U9cLkZ0KaMTu4OW1TSTV+Vw7maCvzYwtqFqlwL4VcCtkSqTheJ9mNMhJpKaFHC3CovSjC/SaPZ0lSNPUJ77+/068B7R3LsP3lK6TEjEQJpzIpx06qmWt8VxTg9zfGlnk7bvT3zuO3k8ePznnvfN4Pcs1EVebLMIeYTB88fvzg/vsDH122vkchZoGuLjxlJ+dEtUKpYjHZJRVRLUWL/FaoK7zYixbssVdyhX1X+ty3377JmB2NVIKyJVLdYFauS/a50jeMxGgSDiRPDIeGr+ABW+lyQcwHCWP/fDCnM+4nIw9aT7QktU54I1ni5VNhGmSC870xrgd9cY2zadb8eEguupE05ihBOg5y2XitmWxwH8HsQffBvTX+8eMb7fsb/+D/5f333zjbxfiyqQE0MiOMdl7QJuPHhKKcoC9/eeXY6s8tztEveofHFVg3uGtYz0e8mvyPuBNjsKbb6lSuyIKUE9ZloxkO8eQBsTToof+mxrl9tLxT0nwiRUgUEBowxwzSMj5PJmmoa+mXE5djZVsumC7Fy5wiN4whQHb31czIH3F1vtASlpfXLrEYq2klFgjXEkuK5dPxro1GpakpXKmo8YCk2B/3TlsRCTkUqQiKV38yiJ/pd7DulMTiz3S8q5mTAsVGrLyZqMaH5cGkHfYhLa+b065KvzrtPBWwA5AzqRSoSya5CA4+Ndyfo68qZZ2+S6ygxlDoZ1kS+WcMY/ynasA+wF1yooTLwZgxtlzWCSV1jj5j/R4Sqr48nD6cdmqBjCHCOhONQUpen/GiR7iiFEhJaXZNSrI5nd6DkZIqoehiCxU5lHIUzJo0i0u2Mlc0hfepyUBeo45s8PQqI+UWw9SktFgUjswYzjlPWkwmoW5/louKWBK+6QwmFn9wizz9rzMA/+XibGdjunNvjTSNOgv751f218/giXkKCxg+qUvEa/sa1t52XmxnOwvMzCS4lwfuiAnTjXEm2nXRx8VMB2GFGrof5q7yJX2qpHmS54OzX4xHI01Zqn6kk8zJ2z9+Jzr4FfB1I78e5N5J3nn/8c6Yzvu4SNM4WsV/+ZX4cnC93RlXo403Yg62pLrLbeDWlfnhm0To20JbngMbjXm9SS1zu5GzDLj26SC2jbO9k3rieN0F4LppxJIxrofTW2Bnpwxnfw2iBJc5bTjX1cWFHRMrhXIznBsR8PjnJVNzn4zPN+YvL9SmrA5//w69YVR8GO//czIeJ9f3b+T9E7cvL+yfXtleKq8vahE+TkULjHlir8Y+PxNFc1w2LfxaFt93H5jv+HjBRib3TBtDiJS+NogG3Qfv17s8QzHJLVFqopaC5UQ1iJLhpZCRqqrkIIVz7xpb/Xj7HfNgXg6RCa+kY+f49Courhl+qdTnCKiBvQIPNdnuaapv8Rjgk52TfS98+vwVIpH8Rk6DUtuH4CNSkaLs+0nkxHhNS1+7Qairfd21Ic23QjL4ekt8+fqF//3//K9wDaIPzlrw65SQPozrXLmpK4TZU9Wd153/+Mdvagr9zOK0rSogNIn9wjVXvN9TAAxpMXC2rUrOlQspyeWvSJhJdg2WJQSQGNpduZn6t6+MidU9dTkbzHwJrTXEJ0QGKLDgxdqVZDjRaROjE+1Btk7EWANzZ5o+HKuiE4QHzEGMLrBX0kmmo+MZprTuuUtwH+FwLklglomWrUqnuZCPFkoqcyYjBUZlXIWMRNDzEcwzyMsbqNNAKExwxhqaY/FxZx75PzF4Fs8n0JjiQ6LIijOYaQ3AddqkbSPvdUGsjapDHgJa2DIEONiKUCzI2VCfOt26vpjGGXmFJoerevAuwX0kxVpND3XqTXTEGZnpmXGt03pIhJ6ryIvmIa5xGDWyQm2f+r4MoM/fFq8o5bU405O99IxhdJXfhtxBFuS8rdN+GZ/ngrJFUom+VyoZpYYLkj6L0J8afSVls0xfghF9zzkbKcP2kihbWr+vVVWUTJpFlU7E0/GGVVE6pmt6EA5X9H8F3/vXi3P75bPQFQz8/mB++4GVQaLDrsbMNkRcY3VMLw8ldEUQ1unWKS2RBsRpOAVKYs6TNk/6lDk2Je3Y3trCdkzMZew2byRvH4t7qxulZvKXTQv5Ebh1Wm/M8wfz+g7FSAkeNpSinLJi+JJ4QON+Z7z9YF4nZRd2xM5Qi6rcsOyQnXqrlG1nhFKi0tsgzMm3A9t2/OUz+XxQ0kUiYyPDKfXK4/0H/diJqGw22em034N+h/qipko+tiVjNXpMpo1lLE5s+yH637jjU4PrCG0Klg9m35e4frDlF8KC93YSZmwpSMfGvit+vmw7txJsNngx3aOvoSiCeTnJMvXlpglWSrDrPt7TahxFIZtx1LTwLJ3x9sY8O7cvr0TJ9FVet+vEluOl2yfMDh7fh0r4LqTI/mnHp+BimwclAvONMSvN1cUut7GuSNtC4qCUMBMj19ftIdrk8k6MBF4opVByptZPJDI9KfTWzxM3zeO3LbO9fKaGXDOXT8Z0bAqXQ61Emoy08DCzY1ZJObG/alEe/6Wy5WBeD3KH1AU1nzv0SzPpVEPa6kNV03VdxAnRlef504szYpBT4vV4ZZC5usTXHoYNEQsCaTFT3iSLm7LjMNX1CoOrN2afPO4/CINSNyyCvVYpVZKMy4Z9RM9NK2AKQ+0rGCasiCh3bItSrgG5jwnTGcca0fiEJKaONuGAOZlx8RZT2InRGA8lHCfbhf+oQBLJwCqaSyWYszGaTN1jJhyJ3XOSI0duiqD7HeIk3yTSHw81uWhOuhnlpZKvLnnXPuXEKUK35JaYw7EmgcBHROEHSlydb0iUKJIpJuVvYIk2Y93BEAEg15VDauK5hrEdhX1LzKR+AXlgU6Y1C93i8+qE2qYTxZtpdLvuokldL6JAOU6JyG83IifynCQysVdYBuc+HgoKXmqiniQxrJ5gRT/mrBL3OMRFTqfIeHnXie0LBgYwveM+eWYBu68Yv2Va9xT4MmQzfqhsrQCTMx7LbwklKiUKxapm0AlGmrS94bbcS4umqPf4D/eOJFWF5AcRhdk7sxsMuNqDazbaXZENdntSJQzvjj8mRiGXxLE/o6V+YnG6d7ay8+vLZ1raeJ+J1pzenNxP7aBVXrxUbtiE0sXiEahZ5eZoF71d3N//A0i8HL9Q68F+3JhWGdlJIVeAlZU1mW76sJnS7KIIwpQy6eUgVaV22Qyiy9iaUPir9Y5lxUZFV0lNd/po3Ftn3O/Mx2MlexnHplDeOCBVo7xqRJRqZvST2Rv93hjNGfO5GalplXg6IZw+70wmx5cv6jZeSafFOcmvhe2XynVNwZJfJr6hRpIbeWRmE+Ij7Uogs6gKwskqsUoKzMTS7TgtLiJVZspKuZ76XsxEkLcx8ctIVf7B47az3wpnMyVm5TcsSyuqptik5MS2F9ImWeNItpplYrimPWNbhZqprxe5NOzTC4RRvt/V8Dp2XGk69OvONYMx1Gnrt4NK4XAjmVChuVZy3dnz8p3+/iBikmvgbvSR8LTsfec7c1zUWcGNOTMkwd+U8eki2s1gXG+6cnFjmnP3O1YqKUMJo3iVKTttbMt/e94easglUzbqgIEx/9PitLRhaSP5C7jTe8O7OvqP8a5cnndtmvtN5ozpwWzBfB+qmvbKy22y5Z9cnCWL/RPpac1SEnDKyGWBUb/8V479xl9fPvHojb+lf3Kd77TznbQgTiNlZt2pxyc1APYbNR/U/MLu7+Cd4VKSzKHdPyGVjpKC0YmqeT2zDcWWXyo1/VyU+DAsVaIY85a13Z8KRSWvvJNo5GOjpKw2uxl5r5J4Fd2rrMk8Pcag3e+M66SfsUpLIAVjXqS08kmSIFrGUt1YkTfNJaruPOjzRj83oCqCoWg4HdfQ9SqQwuelIEvZ0IZliTGlHioVzCSgx3V/ybg8i/d3one4OlE3PL/qrl6HRO8lyagQzvh+x/vKl5wGlrWJ+MBiA8+koeNy2246lUrgffL+/ZLJfC7gX1VeS0SI45sTtsHowWhOFGlZ6012t7rt1KTrUJQN3wtbydL/NhdDeOgvsBpLrC6o15jAI5N6obwKCD7OrBnw1D1cLinRItuK3asPV/juy0b2TJlGM3GjHrVjubDZpvWcEu4AACAASURBVL5HvtRFzoXkUlIt/D91hQ5HCmZ07vc7+xp/5byA2r4vILlCiA11/Pe2phOHcbxk9r3y9fVX9p/FlKSsctMtoegi7eApJ9IUZa/cfmE7Xvj68oq1N2b7Rj8Hj/ZOTQc5bfR8MLOR94NEWqzQKr+kIT1tbEtvqVHER/w4U3khqwGgIbbo3N0vLc4WKouLyg3LSSbgpNGOVnuRbzKv+L20MdfXzZt8hCkvAfcIwoRbGY+Lfp7MrhM9rZhw/0/O+2RCNybyGi0sUbDbGvk0hm+MLkJ9yiZjgCW8z7Ujq9mRalI0whpvYKyGmUu0EAhcNuXnjLT6fe1SqM9j6kXtQ59fEW/omUMzXGaFaI5x6N5hz3gHX2Bsk5bZ7CO6weuk9871eBDvDldw++uL/InrfXmiUK2m5dsceDWixDqhMpRNYUQDwgpeDkoOVQXd8S49bFjwUfGZ+kNzGFxJ99avVVESLS0/6fwY7cQas7Q2maMxJ+RaOI4bEZk0YDAElNsG5D9Ii3EbqjwWzaGkxDSNZGpoE+tJUSGthcYz2wJaJ7l4pm2MOj5YRhIfSKlXNmPfEsdeeN0/s5f95xZnfv0LEYNv3/8H3B3/LmaKlSIinGVGf6fR+X7/zrf24NvbP+nf7vRvE34x/CVpsQF1ewEPRu9CF/LOGJdM2F5RQOym2VUJLahcFsYwVmPCaf2CgOJTd6GK7jh5Lh7qRsQg+lQQ6wwGelm3mkSjs4rVXVK35ORk5G1Tido7YwxZsxKKU8gZi8TNUNpYPPurUrl41j0tG3gJvHXGtwt2Y/xS6OPk/CE3WjiCXJPoY0hiFonIQTWnl4nnQQsJv2e/A8Yor4wZnNcp9UmSZzQsMY4XvCqE2JPiF3NRt1Z5KIl0TzIfpMrcnPZ8CULMXxsS+vdp+PWu+eZndUP770Ks5AKtKoPExkU9B/se6qh+UiUyMGyra8Sk2e+nrUjcUQ9BtnL92HRb/537eGOeCb9Q6GzOjGSM6bTWaI+L8VhNty3RhhEj6G/vEKIasKj0o4ktZRaatVdJQOcca5aq2TFzsr1U8lZ4L1PhvkVNoeIma1jVpunJ+PrphWKZR3T6nNwfpwziDPwceBv0OeluPEIc3IRRPbE3I3nFrPG1Fr4ehZLXafMzi9OsED5p553UII3V1cwLmRsLhBXO+zm491OnTBuwaOhmhvlcDBt5z+dQ8N/EmWMh7j98d2pEYHMBFyTzM9fXk+bhj0EuhvJFzD7sN2uerEHxc/t9jn7MlnVJYxUlaIlZaqXASit2c7kmzPRyT9HvlUcaTOxjgZADinD8Zmus4y7XTJaVymNKVPC0qIUvN4s+GzdBvBKLiYR8nQR4uIQYq7n1kTaeljAjkBqFEHDKDPdBigzLohWB5pHTV0zEChdifR7rVHYk3BitrwgBYUlng49ohmzagBapAfePUnA+P+xkJMvL9S8SRckZX1SFvMkG54jY2HpjDv1dwomqM+8haoRPiSyokmVOf4pSBsa6F/AMUJpYDDWvkpEtf3Cd/ohK0ESghE7++dRXjklKaTXMhHpdehSV31YYK99HWapKhvPeiKszTCMlD5ZGW5LEHPmD6F9LZs9ZG4X/pAghfntnzDtv5z+otnEcn8h1kEowPTGm8fbPk5iD//bt75LvMclWKK8H7BkK5Pc3mJMrZPb1R8AqQ+ccjAn7Ll/hsd0kX+uNsIHbRQwBuHxsTM9YfSOVQPndGfbKPJ3+Y2LnO/SL8lWUbb+tBLCceS5V72q/RwzBs16MyNBozNlo5w8GgZcEu66uTw5N7Gh2VhBE7LV+1FJzamZ7fjsVMdfv5H2jpM/SDmeYpeBkMokYUN6DEc6Zm5pRJhB2pI2UlFvSd82EZzsx0IxwL3BUYsIcHR9vREziuBFzMN9+p6QbpfxCfM70l0ybb3ie5JeASPi3rH7CvqnzWy6svWFDJVtgxPcXwgfz3kSC3ya5bmx1J88hrlKXVNHm1Kx7WcnMYItJns5hFWbw/W9/p5SN/df/QmNyMnj//sZ1v2P+ApiaTAbxrt9XjsSoB/FSeU51/WpYBMdN3e3Yd81ep7N/SkJ4FjkmtnqQyNAqY3bOccfvibDE8eUL28tOfyjJu/0uaSi1MI7MeHHFdsSkb4JhxyxEn4y3dzrGG5lxfzDPi/T6Se9lZGrASwl2jJe0sR87L7df2HJhYPyPf75z9Z/M54wh7SRWIS1WziK06QZjzCmmzmOoXssrVMjKIuUZ65R9OvwFcrZiUFUq2jNCO2nACyjWe8nHCH0d1q6cTOMHFiXBF+EupguFP5FsKonlE+uUebrfYwqvEbbkW54V6zCcOTqjX0p/WqMLS+v0Xg57QF69xYRhORsUo+f4OYlzfkjrJJMz3UFdn5uzTkxBaXFbA/bdVkBSWnJBViUREDrhSbbsc3xI6p6MVJbAzmxVB4vz8zyBEk5ORSXcNfStzURkV7qZT4T6WY25NaKZ8w/BeFqOmJwkr3SXnDONKehZeVZBUHISY6dU8MDnUNzOaHSf9DnwHoTnj4gE0SckRNGno1P9KTc0pF6yHEKZJFEWfS62cJakTqWFJKEKhVKzbaMyd33tvC2pqS2+0wwtxHGRKORUlRNjwWWdaShRbaiRN8Pp4cwYAlFbkMwoJvrEUXc2MjWV5X/V3zHm5Louzp8Nz2UMdbK+/B8UD3Vow8lD6dTTHI8m8npmGWKXamavpCpHyJUOPDm9T7LB/lkQqLQXeN+Ih7g6kZLwIRH0UFNkdoT7qIk89YKWXGW7Milk5nlCm6LiRQZ7ZU4laXl6CNKcIQ2wlhhXpz+a2jYx4Npwg/tDmRjj+o7ljZR3UhSITLIBFnQHZsJbXZ7LA4+L8Iv220X/3hh/exDXxLaEVZ2QqzKFeyOaM1NRQ7fKapWPT8TnTPprJv4xmb/LiBxP7a6po2nJiCyblPugVAkV5vlVwo2rY2Zsr79SyqK8Z2dGY7hhUdnyK8wJ//w73oVstK+v2C+fmDOrETYeWAS3MVe1J+VU8sp2aBTwRKD2ZrrfP5ru3buRgeJwe93Yjw17/ZU5J/Pv/50WjXv7Tjwm8WPCVij1F/ImwJn7KSBczyshw4jmpKbGj1mQ/lJIm7HvGxaFGDv9FJQ710xOmfldKJX5uZMK1P2gzI0bmbE3Rr7YNiPbpKAO7wMYrdPe7lzbxn0/qK8b+Sj8fV6YneRzrF/mxoyLx3xXpMeR2Eqh5ELNla1W/vr5L+QI8ugrOTvo7985rwfvP+6cP3tyzmCJ0SvGgkIlcUH3mgRIH+KwjF7W6API2qmzFVKq7DXhDObpa1C8+pkRCvNZwKPnLi9R8+qyotO6pARPeHtJmkDPDh5LHD2Xe19ZjDbl2Bc+19aACrncdwUnkVHeRVL317c1L2tLFjidgquTuun/fzJkKUANfAzG+6Wy5reT+dbkpLEgl52c98VkXW7+1CFNVQ4ZydJKkQomJZJnSspEdXpuzJjYXDe5rDu7x4VP+SInXR7UleaFX/JZpl3VQtafk8hjwan1i8VuO+SJXwlcLpMxBj4HcwjZKNuTsCJOltXNtIHasvHnLOmkJ8AdvzfyVoUNzRtWNnyejD5kYA/TyEwzs4UQQV4Bgz6DOYPeFnPKxfwRVUJd5JSzUtyk0AVc8fR7/biLe28aGRWDLbBt0SLSquY8iYRhrvc7T1LNei0mlFKpNcuWmIuo8YQaj6G/054ilIqgb4saKFPCxFyw6/5ojBn0GVxt0IYiBd1/cnGOEFS42Ia54b3DIVDx64sG9X4O5pANrPvgjMcqYSdmOyW/su0PPHXO79cyxmpdlBmi6u15pR0vl4hrYejEy+Sc2XIiVcML9E10bt5OlRYz6YXyttQxMMcJwymrLa7SxmEb5Jsp2HWJiVodzBSM3fHLiEde4wSlVG/m2H7AlukLC2lpSt51XVx/u3P9xw/GjwfeB/WmRkjNN2o9yPlGTgujWS6Mju1BKmgsVSp5u8li1owtQ3lBDhh37Mea1ZZDGs/+zpxFxMAZsra9D5iTlN/Wgq8KzElOzhs579KYWhBN/tL0y2c4J+OH7m+8dwYXMzq9XVooHASF6YVkidh0BYgpooMRpCoaxnWqwTXfL/KXxL7doMoM0B//pJ8X85QxutW14IrkcjlYOanONYPenesxlGw+BtuxsR07mZUTWqTsSUjokWMQW2LsL9AvfDTGqYgKXNR3Psk/uefMGCrn59AM2awRxclWpCveNrZUOJKqQCuZvggZmGgdNgvJnELAbtiexCbCIJTizrgzL+f8/eKaweXBPQYtnGsO3H+SW/vj7ZuMzUPMlTont5cX9n2DLPuT0r1iJT6rPLCUoGRe98p+bNRizNH55/c74KQiAtmexLsVN3WIMD6GXkrGGsHljxe4JnUdw4YQiUUsmlT2xZN56L5i6ANe3U9A3bQUykPp8dRkAWiGGIFdk3Q5MTKWK7Zv4rHGOpncYZMczI8VEvzW6Y87s5/YLSgvmbztpFTY8ib8x57WLHCyvRqwskBMdL6UMlijt0k/+7qTQiQnWyy5nTYu3DDPq9Mrno1Zl4QxIN0+KRNl23Qvj6YEaQq0BxaTaBJkPNyZNvByCZXCZLpOQVaIUF4Yin0zclJMAynhlmnjScdTxzNXVTzdG8MLlxfsN7h+L5z9O6N33kPzP2LqXlY1O0wBZRPp4uBG7gM/H0KIWKGUjbpvzIeCmuIqFCrH66sqMB+kp4VxBjYNtl39klrFb5oJN+NBcI07vT04bp/JedMoL2RASMUoNXHUysu2ESZBS7QpAuHQspFHovAlfyJqJkrC343R+xrtJX6URrRJm43mkysG9/uddjVF2/+L518uzvf7DywZZRShMXPG7IVtq3hWS6jPtBwTQx94xJK+FW5b4bYX6kumT9HdwkMICwtqCpIHMZaaZGhWhAV2hCxoOUu5kws5A4TuLjFXTEQmpYN5NeiOZ1dNknZ5/9DOOc4OC+SrJLFYZS0sVSZ2TmiODcGDU71h4yJmZ/RBTIU2kQwvmTmc8f5gXCdzXJRPhbSrRM2pUKkfi9M8wKeMyjljfiyxQscsCMShbW93YjOiGvumnf6Zgh2rsWQL9TlmkOcgoS6lpQzbV9iywN4xxHKdg/CJnyL+zXIjknFWdazJJ+GCegWVQGIBstr/ZoYtdZKNLvGABdHUZKuHGh2pGNaVeTO80bzgv09SN05/Y8TksRp71R1yIeeNNBPJk7rfNbOnILVOy5eaTWNR1bdKf//B6A1rm1wqtZA8xASKwOYaiThQRQ4kLayLJ9ygWdDGyWjf4DhE+G9Fi9OGqritchwbt9smQr0b/WrEMOaUudwyHKnwmiteCpEy7/1BfwxFRWbnrTZ1dmejR6fRedy/0+4n+XZb1I2fWJyvu9QLPgObk9Evzscn3ragzDfMO70nmgfN0f3yeBXXZzPKayLvweP9oj0a+X6SCGoBLPOg8Hg8OM8HeQs4lH2pu3bRPPAZkJoUfJRizeny2tnd2EejZKPeXulMzU97x4eJUTMG5z9/YMXIRwFfJ+6rBuTt211l2hkfJtocQZ6TOYMx4Xp7iDUTu5wLh+h1Nk/yDvyyU27q/NWul22YKUPl/SGnRNkUDVgTo4110i0lzN0Z5+D+7UL8FUifb+SaeVwP3AXHMjMSlTY619l5LUbNibYWb7p3rE28Jdw6007duW2Kgk9wLqxp+9sPSoHbqzJU8/7CeB/M5ti+kWpmq2uO6q7ZatmE3giFJCcgZyXCiQGVKMmJqWjGx9XlfGkdzLj98oW8ZfZbZSvGXoGoEKINpmLc7KCXwfy8DMojsBoMLvoY9GtiQ6KJOBtzBue9MVrHr4aXTOTMnEnOD1Ny2L09aG3y/qOTUyfnyozKtG3NSdWtn73T397pR6HfKmV7wfJGuyajO9mSdMPj4j6NNh5EUkOzjzsenbwUYD/eO345/XtTSnpJ+KxYOLnswtr8zOLcNxlDxyLG4XKm9x5YayS/GFGZAXO6ZpdJwuJcdPeLNGlnp90btK5htYt80EJt6dm7AncSRFELP/LC4c/QKR0rfgBfLffnKaKcS1s2n6eRVX4iKTt8BqN1qWAsiMg6HUInkl8DH4PS1VowU2GZoi8/ojGvoe5oT1h2orlIej41qD6KbFLJSE0CCM9g0/HWcUvLxibBvEleLanbM290LPq46wSYh4QYc0qokWMJQ6wwJ4o+zFWp3rbg2/Mp0AgiTTw5lobGL7rF00JCkP7esN2wl42cC2XbiQdgA6uZXAop6WstFYmCe2YmQk4OTbQkZEiBMKCrYeQz6O7SBg+V76VUatnYys5W5UyLKARZ5Py0AofyYhGlSSpzNcJUVvpi8oSvcd8M2lBkiD/aSj0rOnUjQGme9Ji0Nni8XexHkPYsPXfYhwRxhkKWrlM5qckct0qOlcA9pugeQKCNoz9cgUQGkU8iD4k8SIweeJPKiSjYyotNVtZI6icX56f/6xdiho7p3uE6yYfh+cHcnemJNuDqnfP730l5x6+g7kbZDRq0Uvj2//zG9d64v0/ynsmfFgBp6sPGMnPwIe0LQqZmN1KHuClvKykhDr5I9TKupsbRaOr0LX1sQaLlCM0qI28cv2SCgafls/NJKrs6pfXQYvRtvYMr8Hcvf6BRimM+dJq3YPze9HLMJGTmnqFdxDUZ1x1IDCC7QTct8hqkpjtRGkt/2zLhTovOyEG82FL/GPdHx+4DPFFKIX068O6c3xUctOFYKnjelqIqiDKZ5rSZGZ7J9iIZ3WLVZjN2m3gNtq8vlL2wfT5IOZO8kG8Oh1FusuVZBq5Jen9gR8L2Kl1va7Bpw/EqtUx7m4xppOMmW9+2qbQuTi46cbbilDzUEMw36vFJIy6Hx9lp7vTHResX9+/fBVOrSXar5pTk1JegHpNcuzhDbswccurMDnEAWc4bl+4510zZP+O9sWUnV+AQ+6p1p7rev8dwmcP5RLdCso1+Dowf9Ld/En2yv/wFK5ly7Iw06bNjKw+2lp2UXziXZaxWCJsMO9mssDOJr4WwnXmrumb9zOIsxybmawttUyuINtyZS+RMzqQpqhuh4eyTotaTUrZG63gffwjY7Sk7E2cnZQ3Zgz/kaMYfdwf3UKPCbQXJSqbNGpCr1b4Ueh+tbPto+HgYuRTpYG3q7mS+JGsKjY2kUBwFDa0/bwphCl89/hWpDnx8bbKG56SsNnRHIgcCLwMLxeaFx3LCq9nzjETk+dG6XAzSgUovFtfER/CBJQpTJdEUDShjNMST7bPGDJoZadMjaXaoH2ulRK/P3bZCXlppQhVGLLnZc6QlOqL9EZkOolVM/xC6a2Sl0F53VEE9JXNZJ2oqaoDB82cYzOx4RxXIdBHtp9MeQesX7bqkWU7qVSjucYlQin68sT676UpGl6zkKdlUp8myeheYfle5ZFIBKyvIaM6Pu+oY6+eyRcN4Zt9M6cEZovUZRk5J+a6miiyZU2qV4KJ1PqIKQf0TE2DcS8Jz+tiEf2pxprTh0ZlXxwjytuGz0u+VFoGZ8/LpBtXZEpxX59uPOyMyY2ZSD9yy1Pqvhe32mVSg3IRhDAY1gVFWkefkWT+yTswUWz+ANlU6RVL2SZpgQ3fEmCrnfMocnovhVaZazobjzGLKEeEmBYuFXoxrUpJsSam8gAlrGTT6eDDOd8bjLiFANjq6X9XPt7VgDW8h7ekjwcMYcyLY6wOzDeNV65mhqIfJElSnpaByok0KidfPr1jZsVS5vn1n+LVSuBz/7RIi8vud/KlQvm5QgpE6eQXW1rmTkpOOxyJ0/ie6W0j+lprUPvN1iT9yorXBdQ7y0i3n60Epma9fb2q8/XJIB9uVIO3nlKrGjXRr+Ayu90aYQ6lKQ8tGMSl16r5pEzw3+pw8zjvb5oyb43HH/eLyTHd4uzq9n9y//0+2fef11686/Xcj9SoT+nEwU+HtkfHW8PcfzDD6vkkLG520Ly/Vy4GlRDfHK2xfduwI0gGjSSRyPx9E74x+J5XM9umg2kbtk/F+6VpzaTecqZBz4UgbicaISc6VXI3Xr7+St4Oz/5PRGoxPpEjcts/UUii1cKXGTJMV7f1zi7O/X8zW8bNJJL7t8r3owqN7U1RSNm6fDsiJ+6NJ2Lzke5F03ycSZcvk/Ixem4SJJheh+2YmU3bdKdow3b1cd0zBp7X7xVKmfJyAzw08IauRUnf0Pq7wpFhWekXChcDQfRDT10gDbGPdrZaWdWhI7KFZovRqc3FW5dLw7sSCXAVrJjBFYGAE5MC29fWNj8G9FisQuj/hS9aYl/QOW/OFIJpE6mnZ8lPZyKVSyqa7DgI+J9MYwIqcN7kIsRI+iTnXybbuYabPyj3oTXfd0ZaTA2QedpPqxnRPVWnD+kwEkl7wRX3N9PzZbEklYUsK582lYCTsoWbRHINhFy0ZI86lNKtiAs2u0yy0IedaFiHDlvAEgfMCYgUXR3N1lzdtzukZJfj8jCOYY3GNcaUBtMDPuSI5BiSXCKEWleSChMqfmQLWTHb4JAaMU9GWVmPJFosOnxxspWgMd0EKU5THupfq3eof1dhPLc73//YPKT4eJ3z+THx5UabHOOG8k2KyfYK9bvzy1y/sbyftfq17XBXucOlGAzh2YRHLHDiNSBdzHoxZ2XIh58TxuhEe3N9crgcEZQqKcCJkwi9gkLPGLsNN88uMXsQRcrHDf8ogyRC6a04LZgmmvxGts5VP+uW/rGzL3om5fJzTlfi837CasPwd28BeCn4Oxo8OjwmXE2kQu8Oowjk+BnYE+auRNiMnI82Q99A1klHTS3dXEQzKCoZaJkBzZluJ2H1glqmvX6gvG/txMPuF+2C3poiGL9KVbjlT9p3t9olxPpjtpJ+N2eaHz1Ci+aA95EKZVxewqiz8piW+e8dTcBUN23ek0R0WFG8kT3RxQLA9pB+OTI7E5nCsmMU4ll/3/aTPxvAT7xfD7/QYDCbH9qoN2AcpXI2j46B+eSURpDUCmZfjTdtbegzs6vibkz8XtqNSit6layzlkmuO3nrDh0m3PcDvwOPExqDeIBWRC1LZKMdn0uzSjlclgPq2ERjXfJCG428X3Cp8PogtM8tO3pw9NV5vLzSrvP9wMolPudJxOoN2XlztoqRJ4icXp1QOkG+bXuZ+x8dFeCenTFC5HtK7+k3BL7fjxkzGLLoT8owJt6DUKVFAKtr2xsQirUxK2ZFmJJH6zg4pSIetHqPChaYlLCTtsb7ummtIrEeqldAFlpkFActelsdzfJiO/Qx1mvb6UUoSIa1suBLD1j/RBBizInJa+0fDr8789tAscuWkWEKNkgkx9H35uj9bF20whTrJhCmBrUstwpikcy6Bd2IWdbVTforgneV516Y7gpxXUO26p0dD89u6Tt4s5ZBOtoCs+3fY034X9KHMEh8XUStGxorupWd/KFcTg6LqxxOwwUwa+sc114mnUz/XzPZSuH3Oq3Q3Opu65kcQWad/CiOF2FHJVoMMMWgphVSVwRlLMB9jMCMxc2U8ZDYo/dKF6LVCMY3EDCRcXn2IUAc85tT0wflDflhYFEUgJ5wKIzHeLsS4aWIEjameA6ZqDxhJxg0mxD2wGFyb3tscmZqgbJ2aEuXlSRaEslLWy6Zx4E8uzq4O3+1Qg6O9KSRoDlL+CrYpeboN/OgkM16OG2cKhsUzBWg1foK6yWU+R13ixim05gyhN7CV5Rly9G9gn1RaeAi1GBjWBjYFw8KSNJympk16XsJtPqUFqwmSZX3qXS9Jd+J0Lc6XneQHyTYiNIwnAvPlmsHwdmnwXAIukcWjNXi/w+sL5aWuJoJsZgxUflmSq8En1vJS4vjHAhzXYHTn4QM7T9LbD4KiiIpPG2kr0hJnAdPkWte/bTn8U5XXFg/mpW9DIrLAUpMAIZautkDeN0C0hD6nRgS94eNajSdI28RxHm9vahS1jN02ajl0Tck6dXFnPtYGNv8oqffXyqdfN6zoc3k8Er0N/HBig+w7NhfVwEwfWNN4K+/CYaa0wGvuxNXhbMyaGLnyeNOVpHLXBvDpRYt0jKUhTkDWcjc14xSY5fSp8VuJiW0vUkPV1fDzjTmc+TgJ64R15mxKti6qbtw2zBI9VZE9OsQ5iQsetTOz8/KlsqXMtk/Klti+ZkYTQaLUSopCfsnS/f7U4ly08lJ3wNjQSMBT46iQbXK+B25G66yUqEKMhrdL5mhL7C9f9cv0ovnfQ5F7RzZGFQZippPpmofGCKBLOlgF+/0g000oiz06UJJVFJUttK772YB0U6kbWZ3ItMFMleg3aJ10Dizt2GGUl0yqzji/KTPl/S7SQzLR/HonLXzm08bFFDUuffksmd9WJW3zkLi9Kvw2ZZMowIxgQhnMMvHWsTTp9zszjFw3SBWrLx+KlHxkUjGi6Z6VQnyium3UbaMcCxGaDePQIny8YzahHFgupLSpw9iVB2JZPCFLSdLBrPvizAezJEqd5NLxItPx7fNOTGdesN0Kx5eD5WNjXAobPteJ0IeKhi3dyLYT3DjfOmM0CjfR7sor5p3cT1LNpLoxc2LapP1z4s3VgrXQjLYb0Qw/u2gDm+EJcjcpempdGJaCj8a8upK9aiHV7cNHSwQ1sjb1PFalNoVLJZGGqAZ512Z60XQlmg8i7WDHsushgXs2tk0noA9ntEuNU3swLJPGK+npMy32MdbJ00ibyuD02f7lCvzXi3NdWG2hC0uo3HFz6g7ZgvPU72oODZBzzpQOdTgd8YfKvslx8gRIt9DdSfISdcDsxMMZpzp/S3GlMJ0loonhWAdKgY8xvjSoMSSQ9p7xYXB7ulzWj2ihQJtatci7htAK3jGsBO18MFqnX5eOhpRhuUKUhGbMJ+RryRTzXvX95KzICtfdcylo9QuV/V6eSls4lTlJ05ntlJfztkMuRK0axwzWHVeLW92jjWRZd6q9kG9KJstL2BA+m3prcAAABBpJREFU6f3Hx8ZK0rDb1gjG1q80p+XwqPr+3DUFGhi5nKSkPFVLiZorMZ1hk+0o1L0o4XBCxCWlkeniMd2pi29rZCIq7XHRzs7rfshTmTadzNZWSV7W4nR6Ugm96k4iDY11UmJeU66mWL8aVxdQWEuxrgjD+1xKnBWwlIvK/AhyTig4apUdKeMhJVqaunLlMtaosBOzEb1hdceKMK6GRjCpGPXIIup1iVhGP+k1ETkzfCOZEXX/2NQNwbOpy61z/OsV+K9HKUWp0o/7d2WQbDe2krB8sB2VlIyjd2LCD4zSOsd1yjRtmZ4VhnugqvP9rUksfk3ylij7MmTPzJYPahTK665RSgryltnKhqN4hGs6A+eKIb1s6+rypiA86S57kwvdcsfCSasb6kN/Ju2m0/hzIk0ZnsdyWZxvD3w49Ixt6thlX/iRL58gZc7ffhelvnRSyuSS8N6JR2N2dWnLsZFzYqua9VkRH4mpmSdNGmIniHLITtYr7oPpRe6f2YlDYTjsmsXm+qIXek/kuoy7K4A2bTuWFIIb8JHP4hjtapw/3mCIhbt9GdS9cPt60MtkWmNLhZSMcrwofOmqmBuvXxIT+N71YsVjdcsB+/wqif7ZselsXUC2i5Pz+8k/3n7j8d+/M94a/+Wv/xt12xhJL7cog8a0iblTCcqhoKr+XeXpbJ2UFOWQUqbeYJSuMcQmQX4th2aYVWKO2AbTDbvgVivZNkYTNGAMyWxv9UmUNHxTzqp1W/k1BUMjJeqNdGzYvkHN+JvGYC/loN4qX/7yyvne+dHONcmAuhVKzarKUgZkvt5McLh8q5xMBnc+eaX8bD6npaTB+RyLwaLBfbZ10U+JlJUyPOBD4aNyJRM2iRQfplyfCg0NHxgSCj8pAWndEbyYZpdZ7pWcJCHz5LSke35MuZfdV4NpAYtZIm0WKcGCP1hDc41fEhqFVLBLo5pgnWpDLXIL050trQCbyNqoUtZ/jwXLMIknLELt/IkICctImbJi/awUmOvn8/USLSkiRRc4c1tjqnXSzqdIw1YsgalUy4JVaTSzTupl3Xo2Vp47PKvpE1OsJuuahVqXXS2ntDKCFE9QVgBVqoFdiRSJrUgjbGYyCwx99VgoD7OAJTlM6POeTNEC5uR8fzB/XPTXU7/XWjTieHp3eQItY/1s6+uv014npTYny8ZMQ0SGsthHTxGIPckXIkKozZFWT0NJeApllnjgObeWGd003/bQvXdRCM2WgaBmqH8IM3JKlJSpW2FcTxHCIkSksry76Q8xB2Ib5SR0KD6ImGQy9V/kc1p8SF7+fP58/nz+nZ7/9bL98/nz+fP5//X5c3H++fz5/Js+fy7OP58/n3/T58/F+efz5/Nv+vy5OP98/nz+TZ8/F+efz5/Pv+nz/wFfUmYKndfhbwAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_images_containing_label(ds, label_name):\n",
+        "    assert label_name in ds.class_names\n",
+        "    image_idx = []\n",
+        "    for idx, sample in enumerate(ds):\n",
+        "        labels = ds[idx]['labels']\n",
+        "        label_names = get_label_names(labels, ds.class_names)\n",
+        "        if label_name in label_names:\n",
+        "            image_idx.append(idx)\n",
+        "    return image_idx\n",
+        "\n",
+        "# get all samples with label sea and ocean\n",
+        "images_idx = get_images_containing_label(ds, label_name=\"Sea and ocean\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nUpA1-JP-mnj",
+        "outputId": "5563fb1d-7708-4f04-daa5-424308756277"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# lets display some sea and ocean images, can you see why they don't look like ocean?\n",
+        "for i in range(3):\n",
+        "    display_image(ds, images_idx[i])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 758
+        },
+        "id": "FUtE-CWUAPI2",
+        "outputId": "26e56c61-36c8-4ff1-f761-81f8fcc16085"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Training a model\n",
+        "\n",
+        "\n",
+        "Here we will train a baseline model for 10 epochs on the bigearthnet medium dataset.\n",
+        "\n",
+        "Note that all the logs get printed to screen. \n",
+        "To view results directly in tensorboard, you would have to download the `outputs/` folder and run:\n",
+        "\n",
+        "    tensorboard --logdir outputs/"
+      ],
+      "metadata": {
+        "id": "sozhP_AINJyH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python train.py ++datamodule.dataset_name='bigearthnet-medium' ++datamodule.num_workers=2 ++datamodule.batch_size=256 ++trainer.max_epochs=10 +trainer.accelerator='gpu' +trainer.devices=1"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IlUOy0wEljwz",
+        "outputId": "001b965a-e92f-4efd-ff1e-bdb297285665"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
+            "netCDF4==1.6.1\n",
+            "networkx==2.6.3\n",
+            "nibabel==3.0.2\n",
+            "nltk==3.7\n",
+            "notebook==5.5.0\n",
+            "numba==0.56.2\n",
+            "numcodecs==0.10.2\n",
+            "numexpr==2.8.3\n",
+            "numpy==1.21.6\n",
+            "oauth2client==4.1.3\n",
+            "oauthlib==3.2.1\n",
+            "okgrade==0.4.3\n",
+            "omegaconf==2.2.3\n",
+            "opencv-contrib-python==4.6.0.66\n",
+            "opencv-python==4.6.0.66\n",
+            "opencv-python-headless==4.6.0.66\n",
+            "openpyxl==3.0.10\n",
+            "opt-einsum==3.3.0\n",
+            "osqp==0.6.2.post0\n",
+            "packaging==21.3\n",
+            "palettable==3.3.0\n",
+            "pandas==1.3.5\n",
+            "pandas-datareader==0.9.0\n",
+            "pandas-gbq==0.13.3\n",
+            "pandas-profiling==1.4.1\n",
+            "pandocfilters==1.5.0\n",
+            "panel==0.12.1\n",
+            "param==1.12.2\n",
+            "parso==0.8.3\n",
+            "partd==1.3.0\n",
+            "pastel==0.2.1\n",
+            "pathlib==1.0.1\n",
+            "pathos==0.2.9\n",
+            "pathy==0.6.2\n",
+            "patsy==0.5.3\n",
+            "pep517==0.13.0\n",
+            "pexpect==4.8.0\n",
+            "pickleshare==0.7.5\n",
+            "Pillow==7.1.2\n",
+            "pip==21.1.3\n",
+            "pip-tools==6.2.0\n",
+            "plotly==5.5.0\n",
+            "plotnine==0.8.0\n",
+            "pluggy==0.7.1\n",
+            "pooch==1.6.0\n",
+            "portpicker==1.3.9\n",
+            "pox==0.3.1\n",
+            "ppft==1.7.6.5\n",
+            "prefetch-generator==1.0.1\n",
+            "preshed==3.0.7\n",
+            "prettytable==3.4.1\n",
+            "progressbar2==3.38.0\n",
+            "promise==2.3\n",
+            "prompt-toolkit==2.0.10\n",
+            "prophet==1.1.1\n",
+            "protobuf==3.17.3\n",
+            "psutil==5.4.8\n",
+            "psycopg2==2.9.4\n",
+            "ptyprocess==0.7.0\n",
+            "py==1.11.0\n",
+            "pyarrow==6.0.1\n",
+            "pyasn1==0.4.8\n",
+            "pyasn1-modules==0.2.8\n",
+            "pycocotools==2.0.5\n",
+            "pycparser==2.21\n",
+            "pyct==0.4.8\n",
+            "pydantic==1.9.2\n",
+            "pydata-google-auth==1.4.0\n",
+            "pyDeprecate==0.3.2\n",
+            "pydot==1.3.0\n",
+            "pydot-ng==2.0.0\n",
+            "pydotplus==2.0.2\n",
+            "PyDrive==1.3.1\n",
+            "pyemd==0.5.1\n",
+            "pyerfa==2.0.0.1\n",
+            "Pygments==2.6.1\n",
+            "pygobject==3.26.1\n",
+            "PyJWT==2.5.0\n",
+            "pylev==1.4.0\n",
+            "pymc==4.1.4\n",
+            "PyMeeus==0.5.11\n",
+            "pymongo==4.2.0\n",
+            "pymystem3==0.2.0\n",
+            "PyOpenGL==3.1.6\n",
+            "pyparsing==3.0.9\n",
+            "pyrsistent==0.18.1\n",
+            "pysimdjson==3.2.0\n",
+            "pysndfile==1.3.8\n",
+            "PySocks==1.7.1\n",
+            "pystan==3.3.0\n",
+            "pytest==3.6.4\n",
+            "python-apt==0.0.0\n",
+            "python-chess==0.23.11\n",
+            "python-dateutil==2.8.2\n",
+            "python-louvain==0.16\n",
+            "python-slugify==6.1.2\n",
+            "python-utils==3.3.3\n",
+            "pytorch-lightning==1.7.7\n",
+            "pytz==2022.4\n",
+            "pyviz-comms==2.2.1\n",
+            "PyWavelets==1.3.0\n",
+            "PyYAML==6.0\n",
+            "pyzmq==23.2.1\n",
+            "qdldl==0.1.5.post2\n",
+            "qudida==0.0.4\n",
+            "regex==2022.6.2\n",
+            "requests==2.23.0\n",
+            "requests-oauthlib==1.3.1\n",
+            "resampy==0.4.2\n",
+            "rpy2==3.4.5\n",
+            "rsa==4.9\n",
+            "s3transfer==0.6.0\n",
+            "scikit-image==0.18.3\n",
+            "scikit-learn==1.0.2\n",
+            "scipy==1.7.3\n",
+            "screen-resolution-extra==0.0.0\n",
+            "scs==3.2.0\n",
+            "seaborn==0.11.2\n",
+            "Send2Trash==1.8.0\n",
+            "setuptools==57.4.0\n",
+            "setuptools-git==1.2\n",
+            "Shapely==1.8.4\n",
+            "six==1.15.0\n",
+            "sklearn-pandas==1.8.0\n",
+            "smart-open==5.2.1\n",
+            "smmap==5.0.0\n",
+            "snowballstemmer==2.2.0\n",
+            "sortedcontainers==2.4.0\n",
+            "soundfile==0.11.0\n",
+            "spacy==3.4.1\n",
+            "spacy-legacy==3.0.10\n",
+            "spacy-loggers==1.0.3\n",
+            "Sphinx==1.8.6\n",
+            "sphinxcontrib-serializinghtml==1.1.5\n",
+            "sphinxcontrib-websupport==1.2.4\n",
+            "SQLAlchemy==1.4.41\n",
+            "sqlparse==0.4.3\n",
+            "srsly==2.4.4\n",
+            "statsmodels==0.12.2\n",
+            "sympy==1.7.1\n",
+            "tables==3.7.0\n",
+            "tabulate==0.8.10\n",
+            "tblib==1.7.0\n",
+            "tenacity==8.1.0\n",
+            "tensorboard==2.9.1\n",
+            "tensorboard-data-server==0.6.1\n",
+            "tensorboard-plugin-wit==1.8.1\n",
+            "tensorflow==2.9.2\n",
+            "tensorflow-datasets==4.6.0\n",
+            "tensorflow-estimator==2.9.0\n",
+            "tensorflow-gcs-config==2.9.1\n",
+            "tensorflow-hub==0.12.0\n",
+            "tensorflow-io-gcs-filesystem==0.27.0\n",
+            "tensorflow-metadata==1.10.0\n",
+            "tensorflow-probability==0.16.0\n",
+            "termcolor==2.0.1\n",
+            "terminado==0.13.3\n",
+            "testpath==0.6.0\n",
+            "text-unidecode==1.3\n",
+            "textblob==0.15.3\n",
+            "thinc==8.1.3\n",
+            "threadpoolctl==3.1.0\n",
+            "tifffile==2021.11.2\n",
+            "timm==0.6.11\n",
+            "toml==0.10.2\n",
+            "tomli==2.0.1\n",
+            "toolz==0.12.0\n",
+            "torch @ https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp37-cp37m-linux_x86_64.whl\n",
+            "torch-tb-profiler==0.4.0\n",
+            "torchaudio @ https://download.pytorch.org/whl/cu113/torchaudio-0.12.1%2Bcu113-cp37-cp37m-linux_x86_64.whl\n",
+            "torchmetrics==0.10.0\n",
+            "torchsummary==1.5.1\n",
+            "torchtext==0.13.1\n",
+            "torchvision @ https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp37-cp37m-linux_x86_64.whl\n",
+            "tornado==5.1.1\n",
+            "tqdm==4.64.1\n",
+            "traitlets==5.1.1\n",
+            "tweepy==3.10.0\n",
+            "typeguard==2.7.1\n",
+            "typer==0.4.2\n",
+            "typing-extensions==4.1.1\n",
+            "tzlocal==1.5.1\n",
+            "ujson==5.5.0\n",
+            "uritemplate==3.0.1\n",
+            "urllib3==1.25.11\n",
+            "vega-datasets==0.9.0\n",
+            "wasabi==0.10.1\n",
+            "wcwidth==0.2.5\n",
+            "webargs==8.2.0\n",
+            "webencodings==0.5.1\n",
+            "Werkzeug==1.0.1\n",
+            "wheel==0.37.1\n",
+            "widgetsnbextension==3.6.1\n",
+            "wordcloud==1.8.2.2\n",
+            "wrapt==1.14.1\n",
+            "xarray==0.20.2\n",
+            "xarray-einstats==0.2.2\n",
+            "xgboost==0.90\n",
+            "xkit==0.0.0\n",
+            "xlrd==1.1.0\n",
+            "xlwt==1.3.0\n",
+            "yarl==1.8.1\n",
+            "yellowbrick==1.5\n",
+            "zict==2.2.0\n",
+            "zipp==3.9.0\n",
+            "                  \n",
+            "\n",
+            "[2022-10-13 22:36:41,854][numexpr.utils][INFO] - NumExpr defaulting to 2 threads.\n",
+            "Epoch 0:   0% 0/138 [00:00<?, ?it/s] /content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "Epoch 0:  58% 80/138 [01:13<00:52,  1.10it/s, loss=0.178, v_num=0, loss/train_step=0.173]\n",
+            "Validation: 0it [00:00, ?it/s]\u001b[A/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "\n",
+            "Validation:   0% 0/40 [00:00<?, ?it/s]\u001b[A\n",
+            "Epoch 0:  72% 100/138 [01:26<00:32,  1.15it/s, loss=0.178, v_num=0, loss/train_step=0.173]\n",
+            "Epoch 0:  87% 120/138 [01:33<00:13,  1.29it/s, loss=0.178, v_num=0, loss/train_step=0.173]\n",
+            "Epoch 0: 100% 138/138 [01:38<00:00,  1.40it/s, loss=0.178, v_num=0, loss/train_step=0.173][2022-10-13 22:38:21,081][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    val Epoch: 0\n",
+            "    val Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.00      0.00      0.00       608\n",
+            "                                                                              Airports       0.00      0.00      0.00         5\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00       125\n",
+            "                                                                             Bare rock       0.00      0.00      0.00        24\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00         5\n",
+            "                                                                   Broad-leaved forest       0.00      0.00      0.00      2714\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00         3\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        26\n",
+            "                                                          Complex cultivation patterns       0.00      0.00      0.00      2011\n",
+            "                                                                     Coniferous forest       0.92      0.06      0.11      3116\n",
+            "                                                                    Construction sites       0.00      0.00      0.00         4\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00       205\n",
+            "                                                            Discontinuous urban fabric       0.00      0.00      0.00      1288\n",
+            "                                                                            Dump sites       0.00      0.00      0.00         7\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        17\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00        34\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00         9\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       220\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00        90\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        12\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.00      0.00      0.00      2485\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00        24\n",
+            "                                                                          Mixed forest       0.80      0.03      0.06      3378\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00        96\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       202\n",
+            "                                                             Non-irrigated arable land       0.60      0.46      0.52      3525\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       236\n",
+            "                                                                              Pastures       0.00      0.00      0.00      1977\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00       289\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       277\n",
+            "                                                                            Port areas       0.00      0.00      0.00         3\n",
+            "                                                                           Rice fields       0.00      0.00      0.00        28\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00        13\n",
+            "                                                                               Salines       0.00      0.00      0.00         5\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        11\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       207\n",
+            "                                                                         Sea and ocean       0.87      0.65      0.74      1386\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00         8\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00        32\n",
+            "                                                           Transitional woodland/shrub       0.67      0.00      0.00      2828\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       199\n",
+            "                                                                          Water bodies       0.00      0.00      0.00      1096\n",
+            "                                                                         Water courses       0.00      0.00      0.00       173\n",
+            "\n",
+            "                                                                             micro avg       0.69      0.10      0.17     29001\n",
+            "                                                                             macro avg       0.09      0.03      0.03     29001\n",
+            "                                                                          weighted avg       0.37      0.10      0.12     29001\n",
+            "                                                                           samples avg       0.27      0.17      0.19     29001\n",
+            "\n",
+            "    val Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[9392    0]\n",
+            " [ 608    0]]\n",
+            "\n",
+            "Airports\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[9875    0]\n",
+            " [ 125    0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[7286    0]\n",
+            " [2714    0]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[9974    0]\n",
+            " [  26    0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[7989    0]\n",
+            " [2011    0]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[6867   17]\n",
+            " [2928  188]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[9996    0]\n",
+            " [   4    0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[9795    0]\n",
+            " [ 205    0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[8712    0]\n",
+            " [1288    0]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[9993    0]\n",
+            " [   7    0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[9983    0]\n",
+            " [  17    0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[9966    0]\n",
+            " [  34    0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[9991    0]\n",
+            " [   9    0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[9780    0]\n",
+            " [ 220    0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[9910    0]\n",
+            " [  90    0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[9988    0]\n",
+            " [  12    0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[7515    0]\n",
+            " [2485    0]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[6598   24]\n",
+            " [3279   99]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[9904    0]\n",
+            " [  96    0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[9798    0]\n",
+            " [ 202    0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[5379 1096]\n",
+            " [1899 1626]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[9764    0]\n",
+            " [ 236    0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[8023    0]\n",
+            " [1977    0]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[9711    0]\n",
+            " [ 289    0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[9723    0]\n",
+            " [ 277    0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[9972    0]\n",
+            " [  28    0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[9987    0]\n",
+            " [  13    0]]\n",
+            "\n",
+            "Salines\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[9989    0]\n",
+            " [  11    0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[9793    0]\n",
+            " [ 207    0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[8479  135]\n",
+            " [ 491  895]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[9992    0]\n",
+            " [   8    0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[9968    0]\n",
+            " [  32    0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[7171    1]\n",
+            " [2826    2]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[9801    0]\n",
+            " [ 199    0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[8904    0]\n",
+            " [1096    0]]\n",
+            "\n",
+            "Water courses\n",
+            "[[9827    0]\n",
+            " [ 173    0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 0: 100% 138/138 [01:40<00:00,  1.37it/s, loss=0.173, v_num=0, loss/train_step=0.170, loss/val_step=0.164, loss/val_epoch=0.160]\n",
+            "                                                            \u001b[A[2022-10-13 22:38:23,280][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    train Epoch: 0\n",
+            "    train Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.00      0.00      0.00      1474\n",
+            "                                                                              Airports       0.00      0.00      0.00        60\n",
+            "                                          Annual crops associated with permanent crops       0.01      0.02      0.01       356\n",
+            "                                                                             Bare rock       0.01      0.02      0.01       193\n",
+            "                                                                 Beaches, dunes, sands       0.01      0.02      0.01       104\n",
+            "                                                                   Broad-leaved forest       0.08      0.00      0.00      6897\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00        19\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        83\n",
+            "                                                          Complex cultivation patterns       0.00      0.00      0.00      4932\n",
+            "                                                                     Coniferous forest       0.37      0.24      0.29      8108\n",
+            "                                                                    Construction sites       0.00      0.00      0.00        72\n",
+            "                                                               Continuous urban fabric       0.07      0.00      0.01       503\n",
+            "                                                            Discontinuous urban fabric       0.00      0.00      0.00      3204\n",
+            "                                                                            Dump sites       0.00      0.00      0.00        54\n",
+            "                                                                             Estuaries       0.00      0.10      0.01        59\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00       327\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00       112\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       536\n",
+            "                                                                        Inland marshes       0.02      0.02      0.02       284\n",
+            "                                                                      Intertidal flats       0.00      0.02      0.01        63\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.25      0.04      0.07      6337\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00       310\n",
+            "                                                                          Mixed forest       0.46      0.03      0.06      8480\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00       236\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       562\n",
+            "                                                             Non-irrigated arable land       0.56      0.14      0.22      8784\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       578\n",
+            "                                                                              Pastures       0.13      0.00      0.01      4651\n",
+            "                                                                              Peatbogs       0.02      0.01      0.01       818\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       630\n",
+            "                                                                            Port areas       0.00      0.00      0.00        31\n",
+            "                                                                           Rice fields       0.00      0.00      0.00       260\n",
+            "                                            Road and rail networks and associated land       0.12      0.00      0.01       245\n",
+            "                                                                               Salines       0.00      0.00      0.00        20\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        82\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       553\n",
+            "                                                                         Sea and ocean       0.77      0.19      0.30      3404\n",
+            "                                                              Sparsely vegetated areas       0.03      0.02      0.03        82\n",
+            "                                                          Sport and leisure facilities       0.02      0.01      0.01       395\n",
+            "                                                           Transitional woodland/shrub       0.32      0.01      0.03      7260\n",
+            "                                                                             Vineyards       0.02      0.03      0.03       468\n",
+            "                                                                          Water bodies       0.00      0.00      0.00      2807\n",
+            "                                                                         Water courses       0.00      0.00      0.00       484\n",
+            "\n",
+            "                                                                             micro avg       0.27      0.06      0.10     74917\n",
+            "                                                                             macro avg       0.08      0.02      0.03     74917\n",
+            "                                                                          weighted avg       0.26      0.06      0.09     74917\n",
+            "                                                                           samples avg       0.14      0.08      0.09     74917\n",
+            "\n",
+            "    train Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[23525     1]\n",
+            " [ 1474     0]]\n",
+            "\n",
+            "Airports\n",
+            "[[24750   190]\n",
+            " [   60     0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[23760   884]\n",
+            " [  349     7]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[24498   309]\n",
+            " [  190     3]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[24701   195]\n",
+            " [  102     2]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[18092    11]\n",
+            " [ 6896     1]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[24703   278]\n",
+            " [   19     0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[24906    11]\n",
+            " [   83     0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[20068     0]\n",
+            " [ 4932     0]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[13626  3266]\n",
+            " [ 6186  1922]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[24925     3]\n",
+            " [   72     0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[24472    25]\n",
+            " [  501     2]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[21785    11]\n",
+            " [ 3204     0]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[24946     0]\n",
+            " [   54     0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[23344  1597]\n",
+            " [   53     6]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[24424   249]\n",
+            " [  326     1]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[24885     3]\n",
+            " [  112     0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[24446    18]\n",
+            " [  536     0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[24303   413]\n",
+            " [  277     7]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[24697   240]\n",
+            " [   62     1]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[17900   763]\n",
+            " [ 6087   250]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[24687     3]\n",
+            " [  310     0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[16180   340]\n",
+            " [ 8189   291]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[24738    26]\n",
+            " [  236     0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[24438     0]\n",
+            " [  562     0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[15289   927]\n",
+            " [ 7586  1198]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[24422     0]\n",
+            " [  578     0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[20239   110]\n",
+            " [ 4635    16]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[23960   222]\n",
+            " [  813     5]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[24355    15]\n",
+            " [  630     0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[24647   322]\n",
+            " [   31     0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[24731     9]\n",
+            " [  260     0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[24748     7]\n",
+            " [  244     1]]\n",
+            "\n",
+            "Salines\n",
+            "[[24980     0]\n",
+            " [   20     0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[24898    20]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[24441     6]\n",
+            " [  553     0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[21401   195]\n",
+            " [ 2762   642]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[24845    73]\n",
+            " [   80     2]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[24386   219]\n",
+            " [  391     4]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[17521   219]\n",
+            " [ 7159   101]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[23849   683]\n",
+            " [  452    16]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[22191     2]\n",
+            " [ 2807     0]]\n",
+            "\n",
+            "Water courses\n",
+            "[[24284   232]\n",
+            " [  483     1]]\n",
+            "\n",
+            "    \n",
+            "Epoch 1:   0% 0/138 [00:00<?, ?it/s, loss=0.173, v_num=0, loss/train_step=0.170, loss/val_step=0.164, loss/val_epoch=0.160, loss/train_epoch=0.241]/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "Epoch 1:  58% 80/138 [01:01<00:44,  1.30it/s, loss=0.161, v_num=0, loss/train_step=0.157, loss/val_step=0.164, loss/val_epoch=0.160, loss/train_epoch=0.241]\n",
+            "Validation: 0it [00:00, ?it/s]\u001b[A/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "\n",
+            "Validation:   0% 0/40 [00:00<?, ?it/s]\u001b[A\n",
+            "Epoch 1:  72% 100/138 [01:16<00:28,  1.31it/s, loss=0.161, v_num=0, loss/train_step=0.157, loss/val_step=0.164, loss/val_epoch=0.160, loss/train_epoch=0.241]\n",
+            "Epoch 1:  87% 120/138 [01:22<00:12,  1.46it/s, loss=0.161, v_num=0, loss/train_step=0.157, loss/val_step=0.164, loss/val_epoch=0.160, loss/train_epoch=0.241]\n",
+            "Epoch 1: 100% 138/138 [01:27<00:00,  1.58it/s, loss=0.161, v_num=0, loss/train_step=0.157, loss/val_step=0.164, loss/val_epoch=0.160, loss/train_epoch=0.241][2022-10-13 22:39:51,126][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    val Epoch: 1\n",
+            "    val Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.00      0.00      0.00       608\n",
+            "                                                                              Airports       0.00      0.00      0.00         5\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00       125\n",
+            "                                                                             Bare rock       0.00      0.00      0.00        24\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00         5\n",
+            "                                                                   Broad-leaved forest       0.00      0.00      0.00      2714\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00         3\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        26\n",
+            "                                                          Complex cultivation patterns       0.00      0.00      0.00      2011\n",
+            "                                                                     Coniferous forest       0.73      0.43      0.54      3116\n",
+            "                                                                    Construction sites       0.00      0.00      0.00         4\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00       205\n",
+            "                                                            Discontinuous urban fabric       0.00      0.00      0.00      1288\n",
+            "                                                                            Dump sites       0.00      0.00      0.00         7\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        17\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00        34\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00         9\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       220\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00        90\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        12\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.00      0.00      0.00      2485\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00        24\n",
+            "                                                                          Mixed forest       0.68      0.34      0.45      3378\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00        96\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       202\n",
+            "                                                             Non-irrigated arable land       0.60      0.58      0.59      3525\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       236\n",
+            "                                                                              Pastures       0.73      0.04      0.07      1977\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00       289\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       277\n",
+            "                                                                            Port areas       0.00      0.00      0.00         3\n",
+            "                                                                           Rice fields       0.00      0.00      0.00        28\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00        13\n",
+            "                                                                               Salines       0.00      0.00      0.00         5\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        11\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       207\n",
+            "                                                                         Sea and ocean       0.87      0.82      0.85      1386\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00         8\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00        32\n",
+            "                                                           Transitional woodland/shrub       1.00      0.00      0.00      2828\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       199\n",
+            "                                                                          Water bodies       0.40      0.04      0.07      1096\n",
+            "                                                                         Water courses       0.00      0.00      0.00       173\n",
+            "\n",
+            "                                                                             micro avg       0.68      0.20      0.31     29001\n",
+            "                                                                             macro avg       0.12      0.05      0.06     29001\n",
+            "                                                                          weighted avg       0.43      0.20      0.23     29001\n",
+            "                                                                           samples avg       0.45      0.28      0.33     29001\n",
+            "\n",
+            "    val Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[9392    0]\n",
+            " [ 608    0]]\n",
+            "\n",
+            "Airports\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[9875    0]\n",
+            " [ 125    0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[7286    0]\n",
+            " [2714    0]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[9974    0]\n",
+            " [  26    0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[7989    0]\n",
+            " [2011    0]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[6385  499]\n",
+            " [1791 1325]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[9996    0]\n",
+            " [   4    0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[9795    0]\n",
+            " [ 205    0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[8712    0]\n",
+            " [1288    0]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[9993    0]\n",
+            " [   7    0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[9983    0]\n",
+            " [  17    0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[9966    0]\n",
+            " [  34    0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[9991    0]\n",
+            " [   9    0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[9780    0]\n",
+            " [ 220    0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[9910    0]\n",
+            " [  90    0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[9988    0]\n",
+            " [  12    0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[7515    0]\n",
+            " [2485    0]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[6077  545]\n",
+            " [2223 1155]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[9904    0]\n",
+            " [  96    0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[9798    0]\n",
+            " [ 202    0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[5116 1359]\n",
+            " [1496 2029]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[9764    0]\n",
+            " [ 236    0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[7997   26]\n",
+            " [1907   70]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[9711    0]\n",
+            " [ 289    0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[9723    0]\n",
+            " [ 277    0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[9972    0]\n",
+            " [  28    0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[9987    0]\n",
+            " [  13    0]]\n",
+            "\n",
+            "Salines\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[9989    0]\n",
+            " [  11    0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[9793    0]\n",
+            " [ 207    0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[8440  174]\n",
+            " [ 243 1143]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[9992    0]\n",
+            " [   8    0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[9968    0]\n",
+            " [  32    0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[7172    0]\n",
+            " [2826    2]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[9801    0]\n",
+            " [ 199    0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[8840   64]\n",
+            " [1054   42]]\n",
+            "\n",
+            "Water courses\n",
+            "[[9827    0]\n",
+            " [ 173    0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 1: 100% 138/138 [01:28<00:00,  1.55it/s, loss=0.158, v_num=0, loss/train_step=0.155, loss/val_step=0.145, loss/val_epoch=0.147, loss/train_epoch=0.241]\n",
+            "                                                            \u001b[A[2022-10-13 22:39:52,944][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    train Epoch: 1\n",
+            "    train Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.00      0.00      0.00      1474\n",
+            "                                                                              Airports       0.00      0.00      0.00        60\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00       356\n",
+            "                                                                             Bare rock       0.00      0.00      0.00       193\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00       104\n",
+            "                                                                   Broad-leaved forest       0.42      0.00      0.00      6897\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00        19\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        83\n",
+            "                                                          Complex cultivation patterns       0.00      0.00      0.00      4932\n",
+            "                                                                     Coniferous forest       0.71      0.29      0.41      8108\n",
+            "                                                                    Construction sites       0.00      0.00      0.00        72\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00       503\n",
+            "                                                            Discontinuous urban fabric       0.00      0.00      0.00      3204\n",
+            "                                                                            Dump sites       0.00      0.00      0.00        54\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        59\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00       327\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00       112\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       536\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00       284\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        63\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.26      0.01      0.02      6337\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00       310\n",
+            "                                                                          Mixed forest       0.63      0.28      0.39      8480\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00       236\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       562\n",
+            "                                                             Non-irrigated arable land       0.59      0.51      0.55      8784\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       578\n",
+            "                                                                              Pastures       0.61      0.03      0.06      4651\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00       818\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       630\n",
+            "                                                                            Port areas       0.00      0.00      0.00        31\n",
+            "                                                                           Rice fields       0.00      0.00      0.00       260\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00       245\n",
+            "                                                                               Salines       0.00      0.00      0.00        20\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        82\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       553\n",
+            "                                                                         Sea and ocean       0.84      0.77      0.80      3404\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00        82\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00       395\n",
+            "                                                           Transitional woodland/shrub       0.49      0.03      0.05      7260\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       468\n",
+            "                                                                          Water bodies       0.38      0.02      0.05      2807\n",
+            "                                                                         Water courses       0.00      0.00      0.00       484\n",
+            "\n",
+            "                                                                             micro avg       0.65      0.16      0.26     74917\n",
+            "                                                                             macro avg       0.11      0.05      0.05     74917\n",
+            "                                                                          weighted avg       0.42      0.16      0.20     74917\n",
+            "                                                                           samples avg       0.39      0.25      0.28     74917\n",
+            "\n",
+            "    train Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[23526     0]\n",
+            " [ 1474     0]]\n",
+            "\n",
+            "Airports\n",
+            "[[24940     0]\n",
+            " [   60     0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[24644     0]\n",
+            " [  356     0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[24807     0]\n",
+            " [  193     0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[24896     0]\n",
+            " [  104     0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[18089    14]\n",
+            " [ 6887    10]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[24981     0]\n",
+            " [   19     0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[24917     0]\n",
+            " [   83     0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[20063     5]\n",
+            " [ 4932     0]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[15922   970]\n",
+            " [ 5750  2358]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[24928     0]\n",
+            " [   72     0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[24497     0]\n",
+            " [  503     0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[21796     0]\n",
+            " [ 3204     0]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[24946     0]\n",
+            " [   54     0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[24941     0]\n",
+            " [   59     0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[24673     0]\n",
+            " [  327     0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[24888     0]\n",
+            " [  112     0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[24464     0]\n",
+            " [  536     0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[24716     0]\n",
+            " [  284     0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[24937     0]\n",
+            " [   63     0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[18510   153]\n",
+            " [ 6283    54]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[24690     0]\n",
+            " [  310     0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[15141  1379]\n",
+            " [ 6082  2398]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[24764     0]\n",
+            " [  236     0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[24438     0]\n",
+            " [  562     0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[13138  3078]\n",
+            " [ 4317  4467]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[24422     0]\n",
+            " [  578     0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[20246   103]\n",
+            " [ 4492   159]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[24182     0]\n",
+            " [  818     0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[24370     0]\n",
+            " [  630     0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[24969     0]\n",
+            " [   31     0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[24740     0]\n",
+            " [  260     0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[24755     0]\n",
+            " [  245     0]]\n",
+            "\n",
+            "Salines\n",
+            "[[24980     0]\n",
+            " [   20     0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[24447     0]\n",
+            " [  553     0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[21096   500]\n",
+            " [  789  2615]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[24605     0]\n",
+            " [  395     0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[17549   191]\n",
+            " [ 7078   182]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[24532     0]\n",
+            " [  468     0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[22081   112]\n",
+            " [ 2738    69]]\n",
+            "\n",
+            "Water courses\n",
+            "[[24516     0]\n",
+            " [  484     0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 2:   0% 0/138 [00:00<?, ?it/s, loss=0.158, v_num=0, loss/train_step=0.155, loss/val_step=0.145, loss/val_epoch=0.147, loss/train_epoch=0.163]/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "Epoch 2:  58% 80/138 [01:02<00:45,  1.27it/s, loss=0.152, v_num=0, loss/train_step=0.152, loss/val_step=0.145, loss/val_epoch=0.147, loss/train_epoch=0.163]\n",
+            "Validation: 0it [00:00, ?it/s]\u001b[A/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "\n",
+            "Validation:   0% 0/40 [00:00<?, ?it/s]\u001b[A\n",
+            "Epoch 2:  72% 100/138 [01:16<00:29,  1.30it/s, loss=0.152, v_num=0, loss/train_step=0.152, loss/val_step=0.145, loss/val_epoch=0.147, loss/train_epoch=0.163]\n",
+            "Epoch 2:  87% 120/138 [01:22<00:12,  1.45it/s, loss=0.152, v_num=0, loss/train_step=0.152, loss/val_step=0.145, loss/val_epoch=0.147, loss/train_epoch=0.163]\n",
+            "Epoch 2: 100% 138/138 [01:27<00:00,  1.57it/s, loss=0.152, v_num=0, loss/train_step=0.152, loss/val_step=0.145, loss/val_epoch=0.147, loss/train_epoch=0.163][2022-10-13 22:41:21,130][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    val Epoch: 2\n",
+            "    val Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.71      0.17      0.28       608\n",
+            "                                                                              Airports       0.00      0.00      0.00         5\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00       125\n",
+            "                                                                             Bare rock       0.00      0.00      0.00        24\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00         5\n",
+            "                                                                   Broad-leaved forest       0.64      0.07      0.13      2714\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00         3\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        26\n",
+            "                                                          Complex cultivation patterns       0.00      0.00      0.00      2011\n",
+            "                                                                     Coniferous forest       0.74      0.63      0.68      3116\n",
+            "                                                                    Construction sites       0.00      0.00      0.00         4\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00       205\n",
+            "                                                            Discontinuous urban fabric       0.25      0.00      0.00      1288\n",
+            "                                                                            Dump sites       0.00      0.00      0.00         7\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        17\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00        34\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00         9\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       220\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00        90\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        12\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.40      0.01      0.01      2485\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00        24\n",
+            "                                                                          Mixed forest       0.68      0.60      0.64      3378\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00        96\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       202\n",
+            "                                                             Non-irrigated arable land       0.63      0.57      0.60      3525\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       236\n",
+            "                                                                              Pastures       0.62      0.37      0.46      1977\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00       289\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       277\n",
+            "                                                                            Port areas       0.00      0.00      0.00         3\n",
+            "                                                                           Rice fields       0.00      0.00      0.00        28\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00        13\n",
+            "                                                                               Salines       0.00      0.00      0.00         5\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        11\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       207\n",
+            "                                                                         Sea and ocean       0.89      0.81      0.85      1386\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00         8\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00        32\n",
+            "                                                           Transitional woodland/shrub       0.52      0.19      0.28      2828\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       199\n",
+            "                                                                          Water bodies       0.88      0.01      0.03      1096\n",
+            "                                                                         Water courses       0.00      0.00      0.00       173\n",
+            "\n",
+            "                                                                             micro avg       0.68      0.30      0.42     29001\n",
+            "                                                                             macro avg       0.16      0.08      0.09     29001\n",
+            "                                                                          weighted avg       0.52      0.30      0.34     29001\n",
+            "                                                                           samples avg       0.58      0.39      0.43     29001\n",
+            "\n",
+            "    val Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[9350   42]\n",
+            " [ 503  105]]\n",
+            "\n",
+            "Airports\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[9875    0]\n",
+            " [ 125    0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[7182  104]\n",
+            " [2526  188]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[9974    0]\n",
+            " [  26    0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[7988    1]\n",
+            " [2011    0]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[6200  684]\n",
+            " [1149 1967]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[9996    0]\n",
+            " [   4    0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[9795    0]\n",
+            " [ 205    0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[8709    3]\n",
+            " [1287    1]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[9993    0]\n",
+            " [   7    0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[9983    0]\n",
+            " [  17    0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[9966    0]\n",
+            " [  34    0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[9991    0]\n",
+            " [   9    0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[9780    0]\n",
+            " [ 220    0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[9910    0]\n",
+            " [  90    0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[9988    0]\n",
+            " [  12    0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[7494   21]\n",
+            " [2471   14]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[5674  948]\n",
+            " [1351 2027]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[9904    0]\n",
+            " [  96    0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[9798    0]\n",
+            " [ 202    0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[5270 1205]\n",
+            " [1504 2021]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[9764    0]\n",
+            " [ 236    0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[7565  458]\n",
+            " [1241  736]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[9711    0]\n",
+            " [ 289    0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[9723    0]\n",
+            " [ 277    0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[9972    0]\n",
+            " [  28    0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[9987    0]\n",
+            " [  13    0]]\n",
+            "\n",
+            "Salines\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[9989    0]\n",
+            " [  11    0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[9793    0]\n",
+            " [ 207    0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[8479  135]\n",
+            " [ 264 1122]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[9992    0]\n",
+            " [   8    0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[9968    0]\n",
+            " [  32    0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[6680  492]\n",
+            " [2295  533]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[9801    0]\n",
+            " [ 199    0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[8902    2]\n",
+            " [1082   14]]\n",
+            "\n",
+            "Water courses\n",
+            "[[9827    0]\n",
+            " [ 173    0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 2: 100% 138/138 [01:29<00:00,  1.54it/s, loss=0.15, v_num=0, loss/train_step=0.167, loss/val_step=0.136, loss/val_epoch=0.140, loss/train_epoch=0.163] \n",
+            "                                                            \u001b[A[2022-10-13 22:41:23,345][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    train Epoch: 2\n",
+            "    train Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.75      0.03      0.06      1474\n",
+            "                                                                              Airports       0.00      0.00      0.00        60\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00       356\n",
+            "                                                                             Bare rock       0.00      0.00      0.00       193\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00       104\n",
+            "                                                                   Broad-leaved forest       0.60      0.04      0.08      6897\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00        19\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        83\n",
+            "                                                          Complex cultivation patterns       1.00      0.00      0.00      4932\n",
+            "                                                                     Coniferous forest       0.74      0.57      0.65      8108\n",
+            "                                                                    Construction sites       0.00      0.00      0.00        72\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00       503\n",
+            "                                                            Discontinuous urban fabric       0.25      0.00      0.00      3204\n",
+            "                                                                            Dump sites       0.00      0.00      0.00        54\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        59\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00       327\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00       112\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       536\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00       284\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        63\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.29      0.00      0.01      6337\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00       310\n",
+            "                                                                          Mixed forest       0.68      0.52      0.59      8480\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00       236\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       562\n",
+            "                                                             Non-irrigated arable land       0.61      0.55      0.58      8784\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       578\n",
+            "                                                                              Pastures       0.63      0.15      0.24      4651\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00       818\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       630\n",
+            "                                                                            Port areas       0.00      0.00      0.00        31\n",
+            "                                                                           Rice fields       0.00      0.00      0.00       260\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00       245\n",
+            "                                                                               Salines       0.00      0.00      0.00        20\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        82\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       553\n",
+            "                                                                         Sea and ocean       0.84      0.83      0.84      3404\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00        82\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00       395\n",
+            "                                                           Transitional woodland/shrub       0.55      0.11      0.18      7260\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       468\n",
+            "                                                                          Water bodies       0.41      0.03      0.06      2807\n",
+            "                                                                         Water courses       0.00      0.00      0.00       484\n",
+            "\n",
+            "                                                                             micro avg       0.68      0.25      0.36     74917\n",
+            "                                                                             macro avg       0.17      0.07      0.08     74917\n",
+            "                                                                          weighted avg       0.55      0.25      0.29     74917\n",
+            "                                                                           samples avg       0.52      0.34      0.38     74917\n",
+            "\n",
+            "    train Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[23510    16]\n",
+            " [ 1427    47]]\n",
+            "\n",
+            "Airports\n",
+            "[[24940     0]\n",
+            " [   60     0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[24644     0]\n",
+            " [  356     0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[24807     0]\n",
+            " [  193     0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[24896     0]\n",
+            " [  104     0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[17916   187]\n",
+            " [ 6611   286]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[24981     0]\n",
+            " [   19     0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[24917     0]\n",
+            " [   83     0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[20068     0]\n",
+            " [ 4931     1]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[15269  1623]\n",
+            " [ 3475  4633]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[24928     0]\n",
+            " [   72     0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[24497     0]\n",
+            " [  503     0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[21781    15]\n",
+            " [ 3199     5]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[24946     0]\n",
+            " [   54     0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[24941     0]\n",
+            " [   59     0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[24673     0]\n",
+            " [  327     0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[24888     0]\n",
+            " [  112     0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[24464     0]\n",
+            " [  536     0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[24716     0]\n",
+            " [  284     0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[24937     0]\n",
+            " [   63     0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[18624    39]\n",
+            " [ 6321    16]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[24690     0]\n",
+            " [  310     0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[14425  2095]\n",
+            " [ 4070  4410]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[24764     0]\n",
+            " [  236     0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[24438     0]\n",
+            " [  562     0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[13177  3039]\n",
+            " [ 3991  4793]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[24422     0]\n",
+            " [  578     0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[19940   409]\n",
+            " [ 3964   687]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[24182     0]\n",
+            " [  818     0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[24370     0]\n",
+            " [  630     0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[24969     0]\n",
+            " [   31     0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[24740     0]\n",
+            " [  260     0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[24755     0]\n",
+            " [  245     0]]\n",
+            "\n",
+            "Salines\n",
+            "[[24980     0]\n",
+            " [   20     0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[24447     0]\n",
+            " [  553     0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[21069   527]\n",
+            " [  579  2825]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[24605     0]\n",
+            " [  395     0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[17097   643]\n",
+            " [ 6464   796]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[24532     0]\n",
+            " [  468     0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[22064   129]\n",
+            " [ 2719    88]]\n",
+            "\n",
+            "Water courses\n",
+            "[[24516     0]\n",
+            " [  484     0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 3:   0% 0/138 [00:00<?, ?it/s, loss=0.15, v_num=0, loss/train_step=0.167, loss/val_step=0.136, loss/val_epoch=0.140, loss/train_epoch=0.153]/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "Epoch 3:  58% 80/138 [01:00<00:44,  1.31it/s, loss=0.145, v_num=0, loss/train_step=0.147, loss/val_step=0.136, loss/val_epoch=0.140, loss/train_epoch=0.153]\n",
+            "Validation: 0it [00:00, ?it/s]\u001b[A/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "\n",
+            "Validation:   0% 0/40 [00:00<?, ?it/s]\u001b[A\n",
+            "Epoch 3:  72% 100/138 [01:15<00:28,  1.33it/s, loss=0.145, v_num=0, loss/train_step=0.147, loss/val_step=0.136, loss/val_epoch=0.140, loss/train_epoch=0.153]\n",
+            "Epoch 3:  87% 120/138 [01:20<00:12,  1.48it/s, loss=0.145, v_num=0, loss/train_step=0.147, loss/val_step=0.136, loss/val_epoch=0.140, loss/train_epoch=0.153]\n",
+            "Epoch 3: 100% 138/138 [01:25<00:00,  1.61it/s, loss=0.145, v_num=0, loss/train_step=0.147, loss/val_step=0.136, loss/val_epoch=0.140, loss/train_epoch=0.153][2022-10-13 22:42:49,835][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    val Epoch: 3\n",
+            "    val Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.67      0.26      0.38       608\n",
+            "                                                                              Airports       0.00      0.00      0.00         5\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00       125\n",
+            "                                                                             Bare rock       0.00      0.00      0.00        24\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00         5\n",
+            "                                                                   Broad-leaved forest       0.65      0.07      0.13      2714\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00         3\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        26\n",
+            "                                                          Complex cultivation patterns       0.44      0.01      0.01      2011\n",
+            "                                                                     Coniferous forest       0.76      0.64      0.70      3116\n",
+            "                                                                    Construction sites       0.00      0.00      0.00         4\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00       205\n",
+            "                                                            Discontinuous urban fabric       0.60      0.09      0.15      1288\n",
+            "                                                                            Dump sites       0.00      0.00      0.00         7\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        17\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00        34\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00         9\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       220\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00        90\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        12\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.62      0.00      0.00      2485\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00        24\n",
+            "                                                                          Mixed forest       0.71      0.56      0.62      3378\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00        96\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       202\n",
+            "                                                             Non-irrigated arable land       0.66      0.53      0.59      3525\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       236\n",
+            "                                                                              Pastures       0.68      0.21      0.32      1977\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00       289\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       277\n",
+            "                                                                            Port areas       0.00      0.00      0.00         3\n",
+            "                                                                           Rice fields       0.00      0.00      0.00        28\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00        13\n",
+            "                                                                               Salines       0.00      0.00      0.00         5\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        11\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       207\n",
+            "                                                                         Sea and ocean       0.86      0.89      0.88      1386\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00         8\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00        32\n",
+            "                                                           Transitional woodland/shrub       0.56      0.16      0.25      2828\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       199\n",
+            "                                                                          Water bodies       1.00      0.00      0.00      1096\n",
+            "                                                                         Water courses       0.00      0.00      0.00       173\n",
+            "\n",
+            "                                                                             micro avg       0.71      0.29      0.41     29001\n",
+            "                                                                             macro avg       0.19      0.08      0.09     29001\n",
+            "                                                                          weighted avg       0.61      0.29      0.34     29001\n",
+            "                                                                           samples avg       0.57      0.38      0.43     29001\n",
+            "\n",
+            "    val Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[9314   78]\n",
+            " [ 447  161]]\n",
+            "\n",
+            "Airports\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[9875    0]\n",
+            " [ 125    0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[7179  107]\n",
+            " [2511  203]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[9974    0]\n",
+            " [  26    0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[7974   15]\n",
+            " [1999   12]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[6250  634]\n",
+            " [1112 2004]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[9996    0]\n",
+            " [   4    0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[9795    0]\n",
+            " [ 205    0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[8639   73]\n",
+            " [1178  110]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[9993    0]\n",
+            " [   7    0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[9983    0]\n",
+            " [  17    0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[9966    0]\n",
+            " [  34    0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[9991    0]\n",
+            " [   9    0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[9780    0]\n",
+            " [ 220    0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[9910    0]\n",
+            " [  90    0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[9988    0]\n",
+            " [  12    0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[7512    3]\n",
+            " [2480    5]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[5845  777]\n",
+            " [1499 1879]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[9904    0]\n",
+            " [  96    0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[9798    0]\n",
+            " [ 202    0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[5521  954]\n",
+            " [1654 1871]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[9764    0]\n",
+            " [ 236    0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[7829  194]\n",
+            " [1558  419]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[9711    0]\n",
+            " [ 289    0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[9723    0]\n",
+            " [ 277    0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[9972    0]\n",
+            " [  28    0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[9987    0]\n",
+            " [  13    0]]\n",
+            "\n",
+            "Salines\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[9989    0]\n",
+            " [  11    0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[9793    0]\n",
+            " [ 207    0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[8419  195]\n",
+            " [ 152 1234]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[9992    0]\n",
+            " [   8    0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[9968    0]\n",
+            " [  32    0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[6828  344]\n",
+            " [2383  445]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[9801    0]\n",
+            " [ 199    0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[8904    0]\n",
+            " [1095    1]]\n",
+            "\n",
+            "Water courses\n",
+            "[[9827    0]\n",
+            " [ 173    0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 3: 100% 138/138 [01:26<00:00,  1.60it/s, loss=0.146, v_num=0, loss/train_step=0.140, loss/val_step=0.136, loss/val_epoch=0.137, loss/train_epoch=0.153]\n",
+            "                                                            \u001b[A[2022-10-13 22:42:50,189][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    train Epoch: 3\n",
+            "    train Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.70      0.20      0.31      1474\n",
+            "                                                                              Airports       0.00      0.00      0.00        60\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00       356\n",
+            "                                                                             Bare rock       0.00      0.00      0.00       193\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00       104\n",
+            "                                                                   Broad-leaved forest       0.57      0.09      0.16      6897\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00        19\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        83\n",
+            "                                                          Complex cultivation patterns       0.41      0.01      0.01      4932\n",
+            "                                                                     Coniferous forest       0.75      0.67      0.70      8108\n",
+            "                                                                    Construction sites       0.00      0.00      0.00        72\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00       503\n",
+            "                                                            Discontinuous urban fabric       0.47      0.02      0.04      3204\n",
+            "                                                                            Dump sites       0.00      0.00      0.00        54\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        59\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00       327\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00       112\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       536\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00       284\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        63\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.40      0.01      0.02      6337\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00       310\n",
+            "                                                                          Mixed forest       0.69      0.60      0.64      8480\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00       236\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       562\n",
+            "                                                             Non-irrigated arable land       0.64      0.55      0.59      8784\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       578\n",
+            "                                                                              Pastures       0.63      0.19      0.29      4651\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00       818\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       630\n",
+            "                                                                            Port areas       0.00      0.00      0.00        31\n",
+            "                                                                           Rice fields       0.00      0.00      0.00       260\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00       245\n",
+            "                                                                               Salines       0.00      0.00      0.00        20\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        82\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       553\n",
+            "                                                                         Sea and ocean       0.85      0.86      0.85      3404\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00        82\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00       395\n",
+            "                                                           Transitional woodland/shrub       0.54      0.22      0.31      7260\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       468\n",
+            "                                                                          Water bodies       0.50      0.04      0.07      2807\n",
+            "                                                                         Water courses       0.00      0.00      0.00       484\n",
+            "\n",
+            "                                                                             micro avg       0.69      0.29      0.41     74917\n",
+            "                                                                             macro avg       0.17      0.08      0.09     74917\n",
+            "                                                                          weighted avg       0.53      0.29      0.33     74917\n",
+            "                                                                           samples avg       0.57      0.38      0.43     74917\n",
+            "\n",
+            "    train Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[23398   128]\n",
+            " [ 1176   298]]\n",
+            "\n",
+            "Airports\n",
+            "[[24940     0]\n",
+            " [   60     0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[24644     0]\n",
+            " [  356     0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[24807     0]\n",
+            " [  193     0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[24896     0]\n",
+            " [  104     0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[17624   479]\n",
+            " [ 6273   624]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[24981     0]\n",
+            " [   19     0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[24917     0]\n",
+            " [   83     0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[20027    41]\n",
+            " [ 4904    28]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[15076  1816]\n",
+            " [ 2710  5398]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[24928     0]\n",
+            " [   72     0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[24497     0]\n",
+            " [  503     0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[21713    83]\n",
+            " [ 3130    74]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[24946     0]\n",
+            " [   54     0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[24941     0]\n",
+            " [   59     0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[24673     0]\n",
+            " [  327     0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[24888     0]\n",
+            " [  112     0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[24464     0]\n",
+            " [  536     0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[24716     0]\n",
+            " [  284     0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[24937     0]\n",
+            " [   63     0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[18552   111]\n",
+            " [ 6262    75]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[24690     0]\n",
+            " [  310     0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[14278  2242]\n",
+            " [ 3388  5092]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[24764     0]\n",
+            " [  236     0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[24438     0]\n",
+            " [  562     0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[13534  2682]\n",
+            " [ 3958  4826]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[24422     0]\n",
+            " [  578     0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[19829   520]\n",
+            " [ 3770   881]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[24182     0]\n",
+            " [  818     0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[24370     0]\n",
+            " [  630     0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[24969     0]\n",
+            " [   31     0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[24740     0]\n",
+            " [  260     0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[24755     0]\n",
+            " [  245     0]]\n",
+            "\n",
+            "Salines\n",
+            "[[24980     0]\n",
+            " [   20     0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[24447     0]\n",
+            " [  553     0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[21083   513]\n",
+            " [  482  2922]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[24605     0]\n",
+            " [  395     0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[16410  1330]\n",
+            " [ 5696  1564]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[24532     0]\n",
+            " [  468     0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[22084   109]\n",
+            " [ 2699   108]]\n",
+            "\n",
+            "Water courses\n",
+            "[[24516     0]\n",
+            " [  484     0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 4:   0% 0/138 [00:00<?, ?it/s, loss=0.146, v_num=0, loss/train_step=0.140, loss/val_step=0.136, loss/val_epoch=0.137, loss/train_epoch=0.147]/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "Epoch 4:  58% 80/138 [01:02<00:45,  1.28it/s, loss=0.143, v_num=0, loss/train_step=0.140, loss/val_step=0.136, loss/val_epoch=0.137, loss/train_epoch=0.147]\n",
+            "Validation: 0it [00:00, ?it/s]\u001b[A/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "\n",
+            "Validation:   0% 0/40 [00:00<?, ?it/s]\u001b[A\n",
+            "Epoch 4:  72% 100/138 [01:16<00:29,  1.31it/s, loss=0.143, v_num=0, loss/train_step=0.140, loss/val_step=0.136, loss/val_epoch=0.137, loss/train_epoch=0.147]\n",
+            "Epoch 4:  87% 120/138 [01:22<00:12,  1.46it/s, loss=0.143, v_num=0, loss/train_step=0.140, loss/val_step=0.136, loss/val_epoch=0.137, loss/train_epoch=0.147]\n",
+            "Epoch 4: 100% 138/138 [01:27<00:00,  1.58it/s, loss=0.143, v_num=0, loss/train_step=0.140, loss/val_step=0.136, loss/val_epoch=0.137, loss/train_epoch=0.147][2022-10-13 22:44:18,067][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    val Epoch: 4\n",
+            "    val Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.68      0.19      0.30       608\n",
+            "                                                                              Airports       0.00      0.00      0.00         5\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00       125\n",
+            "                                                                             Bare rock       0.00      0.00      0.00        24\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00         5\n",
+            "                                                                   Broad-leaved forest       0.64      0.04      0.07      2714\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00         3\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        26\n",
+            "                                                          Complex cultivation patterns       0.00      0.00      0.00      2011\n",
+            "                                                                     Coniferous forest       0.76      0.66      0.70      3116\n",
+            "                                                                    Construction sites       0.00      0.00      0.00         4\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00       205\n",
+            "                                                            Discontinuous urban fabric       0.65      0.05      0.10      1288\n",
+            "                                                                            Dump sites       0.00      0.00      0.00         7\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        17\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00        34\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00         9\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       220\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00        90\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        12\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.42      0.02      0.03      2485\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00        24\n",
+            "                                                                          Mixed forest       0.73      0.54      0.62      3378\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00        96\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       202\n",
+            "                                                             Non-irrigated arable land       0.63      0.63      0.63      3525\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       236\n",
+            "                                                                              Pastures       0.64      0.34      0.44      1977\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00       289\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       277\n",
+            "                                                                            Port areas       0.00      0.00      0.00         3\n",
+            "                                                                           Rice fields       0.00      0.00      0.00        28\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00        13\n",
+            "                                                                               Salines       0.00      0.00      0.00         5\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        11\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       207\n",
+            "                                                                         Sea and ocean       0.88      0.88      0.88      1386\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00         8\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00        32\n",
+            "                                                           Transitional woodland/shrub       0.54      0.27      0.37      2828\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       199\n",
+            "                                                                          Water bodies       0.95      0.02      0.03      1096\n",
+            "                                                                         Water courses       0.00      0.00      0.00       173\n",
+            "\n",
+            "                                                                             micro avg       0.69      0.31      0.43     29001\n",
+            "                                                                             macro avg       0.17      0.08      0.10     29001\n",
+            "                                                                          weighted avg       0.56      0.31      0.35     29001\n",
+            "                                                                           samples avg       0.61      0.40      0.45     29001\n",
+            "\n",
+            "    val Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[9337   55]\n",
+            " [ 492  116]]\n",
+            "\n",
+            "Airports\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[9875    0]\n",
+            " [ 125    0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[7232   54]\n",
+            " [2619   95]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[9974    0]\n",
+            " [  26    0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[7988    1]\n",
+            " [2011    0]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[6237  647]\n",
+            " [1072 2044]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[9996    0]\n",
+            " [   4    0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[9795    0]\n",
+            " [ 205    0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[8675   37]\n",
+            " [1219   69]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[9993    0]\n",
+            " [   7    0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[9983    0]\n",
+            " [  17    0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[9966    0]\n",
+            " [  34    0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[9991    0]\n",
+            " [   9    0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[9780    0]\n",
+            " [ 220    0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[9910    0]\n",
+            " [  90    0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[9988    0]\n",
+            " [  12    0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[7463   52]\n",
+            " [2447   38]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[5938  684]\n",
+            " [1562 1816]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[9904    0]\n",
+            " [  96    0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[9798    0]\n",
+            " [ 202    0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[5180 1295]\n",
+            " [1312 2213]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[9764    0]\n",
+            " [ 236    0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[7647  376]\n",
+            " [1312  665]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[9711    0]\n",
+            " [ 289    0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[9723    0]\n",
+            " [ 277    0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[9972    0]\n",
+            " [  28    0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[9987    0]\n",
+            " [  13    0]]\n",
+            "\n",
+            "Salines\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[9989    0]\n",
+            " [  11    0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[9793    0]\n",
+            " [ 207    0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[8441  173]\n",
+            " [ 165 1221]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[9992    0]\n",
+            " [   8    0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[9968    0]\n",
+            " [  32    0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[6521  651]\n",
+            " [2051  777]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[9801    0]\n",
+            " [ 199    0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[8903    1]\n",
+            " [1077   19]]\n",
+            "\n",
+            "Water courses\n",
+            "[[9827    0]\n",
+            " [ 173    0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 4: 100% 138/138 [01:29<00:00,  1.55it/s, loss=0.143, v_num=0, loss/train_step=0.131, loss/val_step=0.132, loss/val_epoch=0.134, loss/train_epoch=0.147]\n",
+            "                                                            \u001b[A[2022-10-13 22:44:19,934][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    train Epoch: 4\n",
+            "    train Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.68      0.25      0.36      1474\n",
+            "                                                                              Airports       0.00      0.00      0.00        60\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00       356\n",
+            "                                                                             Bare rock       0.00      0.00      0.00       193\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00       104\n",
+            "                                                                   Broad-leaved forest       0.58      0.10      0.17      6897\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00        19\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        83\n",
+            "                                                          Complex cultivation patterns       0.49      0.01      0.02      4932\n",
+            "                                                                     Coniferous forest       0.75      0.70      0.72      8108\n",
+            "                                                                    Construction sites       0.00      0.00      0.00        72\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00       503\n",
+            "                                                            Discontinuous urban fabric       0.55      0.07      0.13      3204\n",
+            "                                                                            Dump sites       0.00      0.00      0.00        54\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        59\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00       327\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00       112\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       536\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00       284\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        63\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.36      0.01      0.02      6337\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00       310\n",
+            "                                                                          Mixed forest       0.71      0.62      0.66      8480\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00       236\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       562\n",
+            "                                                             Non-irrigated arable land       0.66      0.55      0.60      8784\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       578\n",
+            "                                                                              Pastures       0.63      0.22      0.32      4651\n",
+            "                                                                              Peatbogs       1.00      0.00      0.00       818\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       630\n",
+            "                                                                            Port areas       0.00      0.00      0.00        31\n",
+            "                                                                           Rice fields       0.00      0.00      0.00       260\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00       245\n",
+            "                                                                               Salines       0.00      0.00      0.00        20\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        82\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       553\n",
+            "                                                                         Sea and ocean       0.85      0.86      0.86      3404\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00        82\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00       395\n",
+            "                                                           Transitional woodland/shrub       0.55      0.25      0.35      7260\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       468\n",
+            "                                                                          Water bodies       0.63      0.04      0.08      2807\n",
+            "                                                                         Water courses       0.00      0.00      0.00       484\n",
+            "\n",
+            "                                                                             micro avg       0.69      0.31      0.43     74917\n",
+            "                                                                             macro avg       0.20      0.09      0.10     74917\n",
+            "                                                                          weighted avg       0.56      0.31      0.35     74917\n",
+            "                                                                           samples avg       0.59      0.40      0.44     74917\n",
+            "\n",
+            "    train Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[23356   170]\n",
+            " [ 1111   363]]\n",
+            "\n",
+            "Airports\n",
+            "[[24940     0]\n",
+            " [   60     0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[24644     0]\n",
+            " [  356     0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[24807     0]\n",
+            " [  193     0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[24896     0]\n",
+            " [  104     0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[17619   484]\n",
+            " [ 6215   682]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[24981     0]\n",
+            " [   19     0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[24917     0]\n",
+            " [   83     0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[20008    60]\n",
+            " [ 4875    57]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[14979  1913]\n",
+            " [ 2451  5657]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[24928     0]\n",
+            " [   72     0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[24497     0]\n",
+            " [  503     0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[21608   188]\n",
+            " [ 2973   231]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[24946     0]\n",
+            " [   54     0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[24941     0]\n",
+            " [   59     0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[24673     0]\n",
+            " [  327     0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[24888     0]\n",
+            " [  112     0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[24464     0]\n",
+            " [  536     0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[24716     0]\n",
+            " [  284     0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[24937     0]\n",
+            " [   63     0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[18522   141]\n",
+            " [ 6257    80]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[24690     0]\n",
+            " [  310     0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[14326  2194]\n",
+            " [ 3216  5264]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[24764     0]\n",
+            " [  236     0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[24438     0]\n",
+            " [  562     0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[13716  2500]\n",
+            " [ 3964  4820]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[24422     0]\n",
+            " [  578     0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[19764   585]\n",
+            " [ 3646  1005]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[24182     0]\n",
+            " [  817     1]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[24370     0]\n",
+            " [  630     0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[24969     0]\n",
+            " [   31     0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[24740     0]\n",
+            " [  260     0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[24755     0]\n",
+            " [  245     0]]\n",
+            "\n",
+            "Salines\n",
+            "[[24980     0]\n",
+            " [   20     0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[24447     0]\n",
+            " [  553     0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[21097   499]\n",
+            " [  467  2937]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[24605     0]\n",
+            " [  395     0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[16217  1523]\n",
+            " [ 5420  1840]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[24532     0]\n",
+            " [  468     0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[22128    65]\n",
+            " [ 2695   112]]\n",
+            "\n",
+            "Water courses\n",
+            "[[24516     0]\n",
+            " [  484     0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 5:   0% 0/138 [00:00<?, ?it/s, loss=0.143, v_num=0, loss/train_step=0.131, loss/val_step=0.132, loss/val_epoch=0.134, loss/train_epoch=0.143]/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "Epoch 5:  58% 80/138 [01:02<00:45,  1.28it/s, loss=0.139, v_num=0, loss/train_step=0.144, loss/val_step=0.132, loss/val_epoch=0.134, loss/train_epoch=0.143]\n",
+            "Validation: 0it [00:00, ?it/s]\u001b[A/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "\n",
+            "Validation:   0% 0/40 [00:00<?, ?it/s]\u001b[A\n",
+            "Epoch 5:  72% 100/138 [01:16<00:29,  1.30it/s, loss=0.139, v_num=0, loss/train_step=0.144, loss/val_step=0.132, loss/val_epoch=0.134, loss/train_epoch=0.143]\n",
+            "Epoch 5:  87% 120/138 [01:22<00:12,  1.45it/s, loss=0.139, v_num=0, loss/train_step=0.144, loss/val_step=0.132, loss/val_epoch=0.134, loss/train_epoch=0.143]\n",
+            "Epoch 5: 100% 138/138 [01:27<00:00,  1.57it/s, loss=0.139, v_num=0, loss/train_step=0.144, loss/val_step=0.132, loss/val_epoch=0.134, loss/train_epoch=0.143][2022-10-13 22:45:48,233][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    val Epoch: 5\n",
+            "    val Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.70      0.18      0.28       608\n",
+            "                                                                              Airports       0.00      0.00      0.00         5\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00       125\n",
+            "                                                                             Bare rock       0.00      0.00      0.00        24\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00         5\n",
+            "                                                                   Broad-leaved forest       0.67      0.04      0.07      2714\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00         3\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        26\n",
+            "                                                          Complex cultivation patterns       0.57      0.02      0.04      2011\n",
+            "                                                                     Coniferous forest       0.72      0.74      0.73      3116\n",
+            "                                                                    Construction sites       0.00      0.00      0.00         4\n",
+            "                                                               Continuous urban fabric       0.50      0.00      0.01       205\n",
+            "                                                            Discontinuous urban fabric       0.59      0.16      0.25      1288\n",
+            "                                                                            Dump sites       0.00      0.00      0.00         7\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        17\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00        34\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00         9\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       220\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00        90\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        12\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.45      0.01      0.02      2485\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00        24\n",
+            "                                                                          Mixed forest       0.70      0.67      0.68      3378\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00        96\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       202\n",
+            "                                                             Non-irrigated arable land       0.72      0.48      0.57      3525\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       236\n",
+            "                                                                              Pastures       0.71      0.18      0.29      1977\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00       289\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       277\n",
+            "                                                                            Port areas       0.00      0.00      0.00         3\n",
+            "                                                                           Rice fields       0.00      0.00      0.00        28\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00        13\n",
+            "                                                                               Salines       0.00      0.00      0.00         5\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        11\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       207\n",
+            "                                                                         Sea and ocean       0.86      0.90      0.88      1386\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00         8\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00        32\n",
+            "                                                           Transitional woodland/shrub       0.52      0.41      0.46      2828\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       199\n",
+            "                                                                          Water bodies       0.76      0.06      0.12      1096\n",
+            "                                                                         Water courses       0.00      0.00      0.00       173\n",
+            "\n",
+            "                                                                             micro avg       0.69      0.33      0.45     29001\n",
+            "                                                                             macro avg       0.20      0.09      0.10     29001\n",
+            "                                                                          weighted avg       0.60      0.33      0.37     29001\n",
+            "                                                                           samples avg       0.60      0.42      0.46     29001\n",
+            "\n",
+            "    val Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[9346   46]\n",
+            " [ 500  108]]\n",
+            "\n",
+            "Airports\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[9875    0]\n",
+            " [ 125    0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[7239   47]\n",
+            " [2617   97]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[9974    0]\n",
+            " [  26    0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[7955   34]\n",
+            " [1966   45]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[5996  888]\n",
+            " [ 817 2299]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[9996    0]\n",
+            " [   4    0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[9794    1]\n",
+            " [ 204    1]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[8568  144]\n",
+            " [1079  209]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[9993    0]\n",
+            " [   7    0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[9983    0]\n",
+            " [  17    0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[9966    0]\n",
+            " [  34    0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[9991    0]\n",
+            " [   9    0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[9780    0]\n",
+            " [ 220    0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[9910    0]\n",
+            " [  90    0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[9988    0]\n",
+            " [  12    0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[7479   36]\n",
+            " [2455   30]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[5658  964]\n",
+            " [1124 2254]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[9904    0]\n",
+            " [  96    0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[9798    0]\n",
+            " [ 202    0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[5823  652]\n",
+            " [1841 1684]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[9764    0]\n",
+            " [ 236    0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[7875  148]\n",
+            " [1614  363]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[9709    2]\n",
+            " [ 289    0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[9723    0]\n",
+            " [ 277    0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[9972    0]\n",
+            " [  28    0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[9987    0]\n",
+            " [  13    0]]\n",
+            "\n",
+            "Salines\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[9989    0]\n",
+            " [  11    0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[9793    0]\n",
+            " [ 207    0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[8418  196]\n",
+            " [ 132 1254]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[9992    0]\n",
+            " [   8    0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[9968    0]\n",
+            " [  32    0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[6123 1049]\n",
+            " [1679 1149]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[9801    0]\n",
+            " [ 199    0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[8882   22]\n",
+            " [1025   71]]\n",
+            "\n",
+            "Water courses\n",
+            "[[9827    0]\n",
+            " [ 173    0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 5: 100% 138/138 [01:29<00:00,  1.54it/s, loss=0.143, v_num=0, loss/train_step=0.148, loss/val_step=0.131, loss/val_epoch=0.133, loss/train_epoch=0.143]\n",
+            "                                                            \u001b[A[2022-10-13 22:45:50,504][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    train Epoch: 5\n",
+            "    train Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.67      0.27      0.38      1474\n",
+            "                                                                              Airports       0.00      0.00      0.00        60\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00       356\n",
+            "                                                                             Bare rock       0.00      0.00      0.00       193\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00       104\n",
+            "                                                                   Broad-leaved forest       0.63      0.10      0.17      6897\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00        19\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        83\n",
+            "                                                          Complex cultivation patterns       0.51      0.02      0.03      4932\n",
+            "                                                                     Coniferous forest       0.76      0.70      0.73      8108\n",
+            "                                                                    Construction sites       0.00      0.00      0.00        72\n",
+            "                                                               Continuous urban fabric       0.50      0.00      0.00       503\n",
+            "                                                            Discontinuous urban fabric       0.58      0.10      0.18      3204\n",
+            "                                                                            Dump sites       0.00      0.00      0.00        54\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        59\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00       327\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00       112\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       536\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00       284\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        63\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.43      0.03      0.06      6337\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00       310\n",
+            "                                                                          Mixed forest       0.71      0.64      0.67      8480\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00       236\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       562\n",
+            "                                                             Non-irrigated arable land       0.67      0.57      0.61      8784\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       578\n",
+            "                                                                              Pastures       0.62      0.24      0.35      4651\n",
+            "                                                                              Peatbogs       0.67      0.00      0.00       818\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       630\n",
+            "                                                                            Port areas       0.00      0.00      0.00        31\n",
+            "                                                                           Rice fields       0.00      0.00      0.00       260\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00       245\n",
+            "                                                                               Salines       0.00      0.00      0.00        20\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        82\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       553\n",
+            "                                                                         Sea and ocean       0.86      0.87      0.86      3404\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00        82\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00       395\n",
+            "                                                           Transitional woodland/shrub       0.56      0.28      0.38      7260\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       468\n",
+            "                                                                          Water bodies       0.66      0.05      0.09      2807\n",
+            "                                                                         Water courses       0.00      0.00      0.00       484\n",
+            "\n",
+            "                                                                             micro avg       0.70      0.32      0.44     74917\n",
+            "                                                                             macro avg       0.21      0.09      0.11     74917\n",
+            "                                                                          weighted avg       0.57      0.32      0.37     74917\n",
+            "                                                                           samples avg       0.61      0.41      0.46     74917\n",
+            "\n",
+            "    train Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[23336   190]\n",
+            " [ 1080   394]]\n",
+            "\n",
+            "Airports\n",
+            "[[24940     0]\n",
+            " [   60     0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[24644     0]\n",
+            " [  356     0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[24807     0]\n",
+            " [  193     0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[24896     0]\n",
+            " [  104     0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[17694   409]\n",
+            " [ 6206   691]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[24981     0]\n",
+            " [   19     0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[24917     0]\n",
+            " [   83     0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[19994    74]\n",
+            " [ 4856    76]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[15059  1833]\n",
+            " [ 2443  5665]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[24928     0]\n",
+            " [   72     0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[24496     1]\n",
+            " [  502     1]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[21550   246]\n",
+            " [ 2870   334]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[24946     0]\n",
+            " [   54     0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[24941     0]\n",
+            " [   59     0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[24673     0]\n",
+            " [  327     0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[24888     0]\n",
+            " [  112     0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[24464     0]\n",
+            " [  536     0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[24716     0]\n",
+            " [  284     0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[24937     0]\n",
+            " [   63     0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[18412   251]\n",
+            " [ 6148   189]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[24690     0]\n",
+            " [  310     0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[14318  2202]\n",
+            " [ 3068  5412]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[24764     0]\n",
+            " [  236     0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[24438     0]\n",
+            " [  562     0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[13772  2444]\n",
+            " [ 3817  4967]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[24422     0]\n",
+            " [  578     0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[19676   673]\n",
+            " [ 3535  1116]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[24181     1]\n",
+            " [  816     2]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[24370     0]\n",
+            " [  630     0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[24969     0]\n",
+            " [   31     0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[24740     0]\n",
+            " [  260     0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[24755     0]\n",
+            " [  245     0]]\n",
+            "\n",
+            "Salines\n",
+            "[[24980     0]\n",
+            " [   20     0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[24447     0]\n",
+            " [  553     0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[21105   491]\n",
+            " [  449  2955]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[24605     0]\n",
+            " [  395     0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[16144  1596]\n",
+            " [ 5191  2069]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[24532     0]\n",
+            " [  468     0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[22121    72]\n",
+            " [ 2665   142]]\n",
+            "\n",
+            "Water courses\n",
+            "[[24516     0]\n",
+            " [  484     0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 6:   0% 0/138 [00:00<?, ?it/s, loss=0.143, v_num=0, loss/train_step=0.148, loss/val_step=0.131, loss/val_epoch=0.133, loss/train_epoch=0.141]/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "Epoch 6:  58% 80/138 [01:01<00:44,  1.30it/s, loss=0.138, v_num=0, loss/train_step=0.137, loss/val_step=0.131, loss/val_epoch=0.133, loss/train_epoch=0.141]\n",
+            "Validation: 0it [00:00, ?it/s]\u001b[A/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "\n",
+            "Validation:   0% 0/40 [00:00<?, ?it/s]\u001b[A\n",
+            "Epoch 6:  72% 100/138 [01:16<00:28,  1.31it/s, loss=0.138, v_num=0, loss/train_step=0.137, loss/val_step=0.131, loss/val_epoch=0.133, loss/train_epoch=0.141]\n",
+            "Epoch 6:  87% 120/138 [01:22<00:12,  1.46it/s, loss=0.138, v_num=0, loss/train_step=0.137, loss/val_step=0.131, loss/val_epoch=0.133, loss/train_epoch=0.141]\n",
+            "Epoch 6: 100% 138/138 [01:27<00:00,  1.58it/s, loss=0.138, v_num=0, loss/train_step=0.137, loss/val_step=0.131, loss/val_epoch=0.133, loss/train_epoch=0.141][2022-10-13 22:47:18,366][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    val Epoch: 6\n",
+            "    val Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.65      0.31      0.42       608\n",
+            "                                                                              Airports       0.00      0.00      0.00         5\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00       125\n",
+            "                                                                             Bare rock       0.00      0.00      0.00        24\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00         5\n",
+            "                                                                   Broad-leaved forest       0.65      0.11      0.19      2714\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00         3\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        26\n",
+            "                                                          Complex cultivation patterns       0.48      0.01      0.01      2011\n",
+            "                                                                     Coniferous forest       0.77      0.65      0.71      3116\n",
+            "                                                                    Construction sites       0.00      0.00      0.00         4\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00       205\n",
+            "                                                            Discontinuous urban fabric       0.60      0.12      0.20      1288\n",
+            "                                                                            Dump sites       0.00      0.00      0.00         7\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        17\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00        34\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00         9\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       220\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00        90\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        12\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.50      0.03      0.06      2485\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00        24\n",
+            "                                                                          Mixed forest       0.72      0.63      0.67      3378\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00        96\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       202\n",
+            "                                                             Non-irrigated arable land       0.66      0.63      0.65      3525\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       236\n",
+            "                                                                              Pastures       0.69      0.25      0.36      1977\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00       289\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       277\n",
+            "                                                                            Port areas       0.00      0.00      0.00         3\n",
+            "                                                                           Rice fields       0.00      0.00      0.00        28\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00        13\n",
+            "                                                                               Salines       0.00      0.00      0.00         5\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        11\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       207\n",
+            "                                                                         Sea and ocean       0.86      0.91      0.88      1386\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00         8\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00        32\n",
+            "                                                           Transitional woodland/shrub       0.61      0.21      0.31      2828\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       199\n",
+            "                                                                          Water bodies       0.80      0.05      0.10      1096\n",
+            "                                                                         Water courses       0.00      0.00      0.00       173\n",
+            "\n",
+            "                                                                             micro avg       0.71      0.33      0.45     29001\n",
+            "                                                                             macro avg       0.19      0.09      0.11     29001\n",
+            "                                                                          weighted avg       0.60      0.33      0.37     29001\n",
+            "                                                                           samples avg       0.63      0.42      0.47     29001\n",
+            "\n",
+            "    val Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[9290  102]\n",
+            " [ 422  186]]\n",
+            "\n",
+            "Airports\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[9875    0]\n",
+            " [ 125    0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[7130  156]\n",
+            " [2421  293]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[9974    0]\n",
+            " [  26    0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[7976   13]\n",
+            " [1999   12]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[6283  601]\n",
+            " [1087 2029]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[9996    0]\n",
+            " [   4    0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[9795    0]\n",
+            " [ 205    0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[8609  103]\n",
+            " [1136  152]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[9993    0]\n",
+            " [   7    0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[9983    0]\n",
+            " [  17    0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[9966    0]\n",
+            " [  34    0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[9991    0]\n",
+            " [   9    0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[9780    0]\n",
+            " [ 220    0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[9910    0]\n",
+            " [  90    0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[9988    0]\n",
+            " [  12    0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[7435   80]\n",
+            " [2404   81]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[5769  853]\n",
+            " [1235 2143]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[9904    0]\n",
+            " [  96    0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[9798    0]\n",
+            " [ 202    0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[5339 1136]\n",
+            " [1304 2221]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[9764    0]\n",
+            " [ 236    0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[7807  216]\n",
+            " [1491  486]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[9711    0]\n",
+            " [ 289    0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[9723    0]\n",
+            " [ 277    0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[9972    0]\n",
+            " [  28    0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[9987    0]\n",
+            " [  13    0]]\n",
+            "\n",
+            "Salines\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[9989    0]\n",
+            " [  11    0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[9793    0]\n",
+            " [ 207    0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[8409  205]\n",
+            " [ 127 1259]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[9992    0]\n",
+            " [   8    0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[9968    0]\n",
+            " [  32    0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[6787  385]\n",
+            " [2236  592]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[9801    0]\n",
+            " [ 199    0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[8890   14]\n",
+            " [1039   57]]\n",
+            "\n",
+            "Water courses\n",
+            "[[9827    0]\n",
+            " [ 173    0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 6: 100% 138/138 [01:29<00:00,  1.54it/s, loss=0.139, v_num=0, loss/train_step=0.135, loss/val_step=0.134, loss/val_epoch=0.131, loss/train_epoch=0.141]\n",
+            "                                                            \u001b[A[2022-10-13 22:47:21,169][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    train Epoch: 6\n",
+            "    train Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.67      0.25      0.36      1474\n",
+            "                                                                              Airports       0.00      0.00      0.00        60\n",
+            "                                          Annual crops associated with permanent crops       1.00      0.00      0.01       356\n",
+            "                                                                             Bare rock       0.00      0.00      0.00       193\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00       104\n",
+            "                                                                   Broad-leaved forest       0.63      0.13      0.22      6897\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00        19\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        83\n",
+            "                                                          Complex cultivation patterns       0.54      0.04      0.07      4932\n",
+            "                                                                     Coniferous forest       0.76      0.70      0.73      8108\n",
+            "                                                                    Construction sites       0.00      0.00      0.00        72\n",
+            "                                                               Continuous urban fabric       1.00      0.01      0.01       503\n",
+            "                                                            Discontinuous urban fabric       0.60      0.15      0.24      3204\n",
+            "                                                                            Dump sites       0.00      0.00      0.00        54\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        59\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00       327\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00       112\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       536\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00       284\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        63\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.50      0.06      0.10      6337\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00       310\n",
+            "                                                                          Mixed forest       0.71      0.64      0.67      8480\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00       236\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       562\n",
+            "                                                             Non-irrigated arable land       0.69      0.58      0.63      8784\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       578\n",
+            "                                                                              Pastures       0.64      0.26      0.37      4651\n",
+            "                                                                              Peatbogs       0.33      0.00      0.00       818\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       630\n",
+            "                                                                            Port areas       0.00      0.00      0.00        31\n",
+            "                                                                           Rice fields       0.00      0.00      0.00       260\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00       245\n",
+            "                                                                               Salines       0.00      0.00      0.00        20\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        82\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       553\n",
+            "                                                                         Sea and ocean       0.86      0.87      0.87      3404\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00        82\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00       395\n",
+            "                                                           Transitional woodland/shrub       0.59      0.31      0.40      7260\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       468\n",
+            "                                                                          Water bodies       0.69      0.07      0.12      2807\n",
+            "                                                                         Water courses       0.00      0.00      0.00       484\n",
+            "\n",
+            "                                                                             micro avg       0.70      0.34      0.45     74917\n",
+            "                                                                             macro avg       0.24      0.09      0.11     74917\n",
+            "                                                                          weighted avg       0.59      0.34      0.39     74917\n",
+            "                                                                           samples avg       0.63      0.42      0.47     74917\n",
+            "\n",
+            "    train Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[23344   182]\n",
+            " [ 1107   367]]\n",
+            "\n",
+            "Airports\n",
+            "[[24940     0]\n",
+            " [   60     0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[24644     0]\n",
+            " [  355     1]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[24807     0]\n",
+            " [  193     0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[24896     0]\n",
+            " [  104     0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[17552   551]\n",
+            " [ 5976   921]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[24981     0]\n",
+            " [   19     0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[24917     0]\n",
+            " [   83     0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[19916   152]\n",
+            " [ 4755   177]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[15095  1797]\n",
+            " [ 2395  5713]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[24928     0]\n",
+            " [   72     0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[24497     0]\n",
+            " [  500     3]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[21471   325]\n",
+            " [ 2715   489]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[24946     0]\n",
+            " [   54     0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[24941     0]\n",
+            " [   59     0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[24673     0]\n",
+            " [  327     0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[24888     0]\n",
+            " [  112     0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[24464     0]\n",
+            " [  536     0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[24716     0]\n",
+            " [  284     0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[24937     0]\n",
+            " [   63     0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[18293   370]\n",
+            " [ 5968   369]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[24690     0]\n",
+            " [  310     0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[14353  2167]\n",
+            " [ 3080  5400]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[24764     0]\n",
+            " [  236     0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[24438     0]\n",
+            " [  562     0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[13877  2339]\n",
+            " [ 3652  5132]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[24422     0]\n",
+            " [  578     0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[19655   694]\n",
+            " [ 3438  1213]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[24178     4]\n",
+            " [  816     2]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[24370     0]\n",
+            " [  630     0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[24969     0]\n",
+            " [   31     0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[24740     0]\n",
+            " [  260     0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[24755     0]\n",
+            " [  245     0]]\n",
+            "\n",
+            "Salines\n",
+            "[[24980     0]\n",
+            " [   20     0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[24447     0]\n",
+            " [  553     0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[21126   470]\n",
+            " [  434  2970]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[24605     0]\n",
+            " [  395     0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[16178  1562]\n",
+            " [ 5033  2227]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[24532     0]\n",
+            " [  468     0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[22111    82]\n",
+            " [ 2621   186]]\n",
+            "\n",
+            "Water courses\n",
+            "[[24516     0]\n",
+            " [  484     0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 7:   0% 0/138 [00:00<?, ?it/s, loss=0.139, v_num=0, loss/train_step=0.135, loss/val_step=0.134, loss/val_epoch=0.131, loss/train_epoch=0.139]/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "Epoch 7:  58% 80/138 [01:01<00:44,  1.30it/s, loss=0.136, v_num=0, loss/train_step=0.136, loss/val_step=0.134, loss/val_epoch=0.131, loss/train_epoch=0.139]\n",
+            "Validation: 0it [00:00, ?it/s]\u001b[A/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "\n",
+            "Validation:   0% 0/40 [00:00<?, ?it/s]\u001b[A\n",
+            "Epoch 7:  72% 100/138 [01:16<00:28,  1.32it/s, loss=0.136, v_num=0, loss/train_step=0.136, loss/val_step=0.134, loss/val_epoch=0.131, loss/train_epoch=0.139]\n",
+            "Epoch 7:  87% 120/138 [01:22<00:12,  1.46it/s, loss=0.136, v_num=0, loss/train_step=0.136, loss/val_step=0.134, loss/val_epoch=0.131, loss/train_epoch=0.139]\n",
+            "Epoch 7: 100% 138/138 [01:27<00:00,  1.58it/s, loss=0.136, v_num=0, loss/train_step=0.136, loss/val_step=0.134, loss/val_epoch=0.131, loss/train_epoch=0.139][2022-10-13 22:48:48,897][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    val Epoch: 7\n",
+            "    val Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.63      0.32      0.43       608\n",
+            "                                                                              Airports       0.00      0.00      0.00         5\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00       125\n",
+            "                                                                             Bare rock       0.00      0.00      0.00        24\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00         5\n",
+            "                                                                   Broad-leaved forest       0.66      0.18      0.28      2714\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00         3\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        26\n",
+            "                                                          Complex cultivation patterns       0.53      0.00      0.01      2011\n",
+            "                                                                     Coniferous forest       0.75      0.72      0.74      3116\n",
+            "                                                                    Construction sites       0.00      0.00      0.00         4\n",
+            "                                                               Continuous urban fabric       1.00      0.00      0.01       205\n",
+            "                                                            Discontinuous urban fabric       0.62      0.12      0.20      1288\n",
+            "                                                                            Dump sites       0.00      0.00      0.00         7\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        17\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00        34\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00         9\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       220\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00        90\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        12\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.55      0.01      0.03      2485\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00        24\n",
+            "                                                                          Mixed forest       0.71      0.64      0.68      3378\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00        96\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       202\n",
+            "                                                             Non-irrigated arable land       0.72      0.57      0.64      3525\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       236\n",
+            "                                                                              Pastures       0.66      0.33      0.44      1977\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00       289\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       277\n",
+            "                                                                            Port areas       0.00      0.00      0.00         3\n",
+            "                                                                           Rice fields       0.00      0.00      0.00        28\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00        13\n",
+            "                                                                               Salines       0.00      0.00      0.00         5\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        11\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       207\n",
+            "                                                                         Sea and ocean       0.88      0.90      0.89      1386\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00         8\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00        32\n",
+            "                                                           Transitional woodland/shrub       0.60      0.22      0.32      2828\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       199\n",
+            "                                                                          Water bodies       0.78      0.08      0.14      1096\n",
+            "                                                                         Water courses       0.00      0.00      0.00       173\n",
+            "\n",
+            "                                                                             micro avg       0.72      0.34      0.46     29001\n",
+            "                                                                             macro avg       0.21      0.10      0.11     29001\n",
+            "                                                                          weighted avg       0.62      0.34      0.39     29001\n",
+            "                                                                           samples avg       0.64      0.43      0.48     29001\n",
+            "\n",
+            "    val Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[9277  115]\n",
+            " [ 411  197]]\n",
+            "\n",
+            "Airports\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[9875    0]\n",
+            " [ 125    0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[7041  245]\n",
+            " [2237  477]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[9974    0]\n",
+            " [  26    0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[7980    9]\n",
+            " [2001   10]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[6131  753]\n",
+            " [ 868 2248]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[9996    0]\n",
+            " [   4    0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[9795    0]\n",
+            " [ 204    1]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[8620   92]\n",
+            " [1136  152]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[9993    0]\n",
+            " [   7    0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[9983    0]\n",
+            " [  17    0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[9966    0]\n",
+            " [  34    0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[9991    0]\n",
+            " [   9    0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[9780    0]\n",
+            " [ 220    0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[9910    0]\n",
+            " [  90    0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[9988    0]\n",
+            " [  12    0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[7485   30]\n",
+            " [2448   37]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[5751  871]\n",
+            " [1209 2169]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[9904    0]\n",
+            " [  96    0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[9798    0]\n",
+            " [ 202    0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[5674  801]\n",
+            " [1503 2022]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[9764    0]\n",
+            " [ 236    0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[7690  333]\n",
+            " [1316  661]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[9704    7]\n",
+            " [ 289    0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[9723    0]\n",
+            " [ 277    0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[9972    0]\n",
+            " [  28    0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[9987    0]\n",
+            " [  13    0]]\n",
+            "\n",
+            "Salines\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[9989    0]\n",
+            " [  11    0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[9793    0]\n",
+            " [ 207    0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[8444  170]\n",
+            " [ 145 1241]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[9992    0]\n",
+            " [   8    0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[9968    0]\n",
+            " [  32    0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[6763  409]\n",
+            " [2203  625]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[9801    0]\n",
+            " [ 199    0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[8880   24]\n",
+            " [1010   86]]\n",
+            "\n",
+            "Water courses\n",
+            "[[9827    0]\n",
+            " [ 173    0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 7: 100% 138/138 [01:29<00:00,  1.55it/s, loss=0.136, v_num=0, loss/train_step=0.136, loss/val_step=0.130, loss/val_epoch=0.129, loss/train_epoch=0.139]\n",
+            "                                                            \u001b[A[2022-10-13 22:48:51,133][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    train Epoch: 7\n",
+            "    train Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.66      0.25      0.36      1474\n",
+            "                                                                              Airports       0.00      0.00      0.00        60\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00       356\n",
+            "                                                                             Bare rock       0.00      0.00      0.00       193\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00       104\n",
+            "                                                                   Broad-leaved forest       0.66      0.15      0.25      6897\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00        19\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        83\n",
+            "                                                          Complex cultivation patterns       0.51      0.05      0.09      4932\n",
+            "                                                                     Coniferous forest       0.77      0.70      0.73      8108\n",
+            "                                                                    Construction sites       0.00      0.00      0.00        72\n",
+            "                                                               Continuous urban fabric       1.00      0.01      0.01       503\n",
+            "                                                            Discontinuous urban fabric       0.60      0.17      0.27      3204\n",
+            "                                                                            Dump sites       0.00      0.00      0.00        54\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        59\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00       327\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00       112\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       536\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00       284\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        63\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.53      0.09      0.16      6337\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00       310\n",
+            "                                                                          Mixed forest       0.71      0.65      0.68      8480\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00       236\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       562\n",
+            "                                                             Non-irrigated arable land       0.69      0.60      0.64      8784\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       578\n",
+            "                                                                              Pastures       0.65      0.27      0.38      4651\n",
+            "                                                                              Peatbogs       0.60      0.01      0.01       818\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       630\n",
+            "                                                                            Port areas       0.00      0.00      0.00        31\n",
+            "                                                                           Rice fields       0.00      0.00      0.00       260\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00       245\n",
+            "                                                                               Salines       0.00      0.00      0.00        20\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        82\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       553\n",
+            "                                                                         Sea and ocean       0.87      0.87      0.87      3404\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00        82\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00       395\n",
+            "                                                           Transitional woodland/shrub       0.59      0.31      0.41      7260\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       468\n",
+            "                                                                          Water bodies       0.68      0.09      0.16      2807\n",
+            "                                                                         Water courses       0.00      0.00      0.00       484\n",
+            "\n",
+            "                                                                             micro avg       0.70      0.35      0.47     74917\n",
+            "                                                                             macro avg       0.22      0.10      0.12     74917\n",
+            "                                                                          weighted avg       0.60      0.35      0.40     74917\n",
+            "                                                                           samples avg       0.64      0.43      0.48     74917\n",
+            "\n",
+            "    train Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[23340   186]\n",
+            " [ 1109   365]]\n",
+            "\n",
+            "Airports\n",
+            "[[24940     0]\n",
+            " [   60     0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[24643     1]\n",
+            " [  356     0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[24807     0]\n",
+            " [  193     0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[24896     0]\n",
+            " [  104     0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[17549   554]\n",
+            " [ 5837  1060]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[24981     0]\n",
+            " [   19     0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[24917     0]\n",
+            " [   83     0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[19829   239]\n",
+            " [ 4684   248]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[15145  1747]\n",
+            " [ 2393  5715]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[24928     0]\n",
+            " [   72     0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[24497     0]\n",
+            " [  500     3]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[21436   360]\n",
+            " [ 2654   550]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[24946     0]\n",
+            " [   54     0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[24941     0]\n",
+            " [   59     0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[24673     0]\n",
+            " [  327     0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[24888     0]\n",
+            " [  112     0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[24464     0]\n",
+            " [  536     0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[24716     0]\n",
+            " [  284     0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[24937     0]\n",
+            " [   63     0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[18156   507]\n",
+            " [ 5757   580]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[24690     0]\n",
+            " [  310     0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[14292  2228]\n",
+            " [ 2981  5499]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[24764     0]\n",
+            " [  236     0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[24438     0]\n",
+            " [  562     0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[13858  2358]\n",
+            " [ 3494  5290]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[24422     0]\n",
+            " [  578     0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[19677   672]\n",
+            " [ 3413  1238]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[24178     4]\n",
+            " [  812     6]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[24370     0]\n",
+            " [  630     0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[24969     0]\n",
+            " [   31     0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[24740     0]\n",
+            " [  260     0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[24755     0]\n",
+            " [  245     0]]\n",
+            "\n",
+            "Salines\n",
+            "[[24980     0]\n",
+            " [   20     0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[24447     0]\n",
+            " [  553     0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[21134   462]\n",
+            " [  439  2965]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[24605     0]\n",
+            " [  395     0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[16188  1552]\n",
+            " [ 4997  2263]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[24532     0]\n",
+            " [  468     0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[22072   121]\n",
+            " [ 2549   258]]\n",
+            "\n",
+            "Water courses\n",
+            "[[24516     0]\n",
+            " [  484     0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 8:   0% 0/138 [00:00<?, ?it/s, loss=0.136, v_num=0, loss/train_step=0.136, loss/val_step=0.130, loss/val_epoch=0.129, loss/train_epoch=0.137]/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "Epoch 8:  58% 80/138 [01:03<00:45,  1.26it/s, loss=0.135, v_num=0, loss/train_step=0.138, loss/val_step=0.130, loss/val_epoch=0.129, loss/train_epoch=0.137]\n",
+            "Validation: 0it [00:00, ?it/s]\u001b[A/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "\n",
+            "Validation:   0% 0/40 [00:00<?, ?it/s]\u001b[A\n",
+            "Epoch 8:  72% 100/138 [01:17<00:29,  1.29it/s, loss=0.135, v_num=0, loss/train_step=0.138, loss/val_step=0.130, loss/val_epoch=0.129, loss/train_epoch=0.137]\n",
+            "Epoch 8:  87% 120/138 [01:23<00:12,  1.43it/s, loss=0.135, v_num=0, loss/train_step=0.138, loss/val_step=0.130, loss/val_epoch=0.129, loss/train_epoch=0.137]\n",
+            "Epoch 8: 100% 138/138 [01:28<00:00,  1.56it/s, loss=0.135, v_num=0, loss/train_step=0.138, loss/val_step=0.130, loss/val_epoch=0.129, loss/train_epoch=0.137][2022-10-13 22:50:20,414][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    val Epoch: 8\n",
+            "    val Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.70      0.20      0.31       608\n",
+            "                                                                              Airports       0.00      0.00      0.00         5\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00       125\n",
+            "                                                                             Bare rock       0.00      0.00      0.00        24\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00         5\n",
+            "                                                                   Broad-leaved forest       0.63      0.25      0.35      2714\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00         3\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        26\n",
+            "                                                          Complex cultivation patterns       0.55      0.06      0.11      2011\n",
+            "                                                                     Coniferous forest       0.80      0.62      0.70      3116\n",
+            "                                                                    Construction sites       0.00      0.00      0.00         4\n",
+            "                                                               Continuous urban fabric       0.00      0.00      0.00       205\n",
+            "                                                            Discontinuous urban fabric       0.60      0.24      0.34      1288\n",
+            "                                                                            Dump sites       0.00      0.00      0.00         7\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        17\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00        34\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00         9\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       220\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00        90\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        12\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.62      0.05      0.09      2485\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00        24\n",
+            "                                                                          Mixed forest       0.75      0.47      0.58      3378\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00        96\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       202\n",
+            "                                                             Non-irrigated arable land       0.72      0.58      0.64      3525\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       236\n",
+            "                                                                              Pastures       0.74      0.22      0.34      1977\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00       289\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       277\n",
+            "                                                                            Port areas       0.00      0.00      0.00         3\n",
+            "                                                                           Rice fields       0.00      0.00      0.00        28\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00        13\n",
+            "                                                                               Salines       0.00      0.00      0.00         5\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        11\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       207\n",
+            "                                                                         Sea and ocean       0.89      0.90      0.90      1386\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00         8\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00        32\n",
+            "                                                           Transitional woodland/shrub       0.61      0.17      0.27      2828\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       199\n",
+            "                                                                          Water bodies       0.80      0.12      0.21      1096\n",
+            "                                                                         Water courses       0.00      0.00      0.00       173\n",
+            "\n",
+            "                                                                             micro avg       0.74      0.32      0.44     29001\n",
+            "                                                                             macro avg       0.20      0.09      0.11     29001\n",
+            "                                                                          weighted avg       0.63      0.32      0.39     29001\n",
+            "                                                                           samples avg       0.63      0.41      0.46     29001\n",
+            "\n",
+            "    val Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[9340   52]\n",
+            " [ 487  121]]\n",
+            "\n",
+            "Airports\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[9875    0]\n",
+            " [ 125    0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[6892  394]\n",
+            " [2047  667]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[9974    0]\n",
+            " [  26    0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[7885  104]\n",
+            " [1886  125]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[6389  495]\n",
+            " [1178 1938]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[9996    0]\n",
+            " [   4    0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[9795    0]\n",
+            " [ 205    0]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[8506  206]\n",
+            " [ 979  309]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[9993    0]\n",
+            " [   7    0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[9983    0]\n",
+            " [  17    0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[9966    0]\n",
+            " [  34    0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[9991    0]\n",
+            " [   9    0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[9780    0]\n",
+            " [ 220    0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[9910    0]\n",
+            " [  90    0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[9988    0]\n",
+            " [  12    0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[7442   73]\n",
+            " [2367  118]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[6100  522]\n",
+            " [1796 1582]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[9904    0]\n",
+            " [  96    0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[9798    0]\n",
+            " [ 202    0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[5681  794]\n",
+            " [1478 2047]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[9764    0]\n",
+            " [ 236    0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[7869  154]\n",
+            " [1539  438]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[9711    0]\n",
+            " [ 289    0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[9723    0]\n",
+            " [ 277    0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[9972    0]\n",
+            " [  28    0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[9987    0]\n",
+            " [  13    0]]\n",
+            "\n",
+            "Salines\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[9989    0]\n",
+            " [  11    0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[9793    0]\n",
+            " [ 207    0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[8464  150]\n",
+            " [ 140 1246]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[9992    0]\n",
+            " [   8    0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[9968    0]\n",
+            " [  32    0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[6866  306]\n",
+            " [2344  484]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[9801    0]\n",
+            " [ 199    0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[8871   33]\n",
+            " [ 966  130]]\n",
+            "\n",
+            "Water courses\n",
+            "[[9827    0]\n",
+            " [ 173    0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 8: 100% 138/138 [01:28<00:00,  1.55it/s, loss=0.135, v_num=0, loss/train_step=0.128, loss/val_step=0.130, loss/val_epoch=0.129, loss/train_epoch=0.137]\n",
+            "                                                            \u001b[A[2022-10-13 22:50:20,767][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    train Epoch: 8\n",
+            "    train Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.65      0.24      0.35      1474\n",
+            "                                                                              Airports       0.00      0.00      0.00        60\n",
+            "                                          Annual crops associated with permanent crops       1.00      0.01      0.01       356\n",
+            "                                                                             Bare rock       0.00      0.00      0.00       193\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00       104\n",
+            "                                                                   Broad-leaved forest       0.66      0.20      0.31      6897\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00        19\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        83\n",
+            "                                                          Complex cultivation patterns       0.58      0.07      0.13      4932\n",
+            "                                                                     Coniferous forest       0.77      0.71      0.74      8108\n",
+            "                                                                    Construction sites       0.00      0.00      0.00        72\n",
+            "                                                               Continuous urban fabric       0.57      0.01      0.02       503\n",
+            "                                                            Discontinuous urban fabric       0.62      0.20      0.30      3204\n",
+            "                                                                            Dump sites       0.00      0.00      0.00        54\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        59\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00       327\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00       112\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       536\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00       284\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        63\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.57      0.13      0.21      6337\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00       310\n",
+            "                                                                          Mixed forest       0.72      0.65      0.69      8480\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00       236\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       562\n",
+            "                                                             Non-irrigated arable land       0.70      0.61      0.66      8784\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       578\n",
+            "                                                                              Pastures       0.65      0.29      0.40      4651\n",
+            "                                                                              Peatbogs       0.62      0.01      0.02       818\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       630\n",
+            "                                                                            Port areas       0.00      0.00      0.00        31\n",
+            "                                                                           Rice fields       0.00      0.00      0.00       260\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00       245\n",
+            "                                                                               Salines       0.00      0.00      0.00        20\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        82\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       553\n",
+            "                                                                         Sea and ocean       0.87      0.87      0.87      3404\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00        82\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00       395\n",
+            "                                                           Transitional woodland/shrub       0.59      0.32      0.42      7260\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       468\n",
+            "                                                                          Water bodies       0.71      0.12      0.20      2807\n",
+            "                                                                         Water courses       0.00      0.00      0.00       484\n",
+            "\n",
+            "                                                                             micro avg       0.71      0.36      0.48     74917\n",
+            "                                                                             macro avg       0.24      0.10      0.12     74917\n",
+            "                                                                          weighted avg       0.61      0.36      0.42     74917\n",
+            "                                                                           samples avg       0.65      0.45      0.50     74917\n",
+            "\n",
+            "    train Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[23337   189]\n",
+            " [ 1117   357]]\n",
+            "\n",
+            "Airports\n",
+            "[[24940     0]\n",
+            " [   60     0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[24644     0]\n",
+            " [  354     2]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[24807     0]\n",
+            " [  193     0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[24896     0]\n",
+            " [  104     0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[17371   732]\n",
+            " [ 5487  1410]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[24981     0]\n",
+            " [   19     0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[24917     0]\n",
+            " [   83     0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[19818   250]\n",
+            " [ 4584   348]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[15170  1722]\n",
+            " [ 2323  5785]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[24928     0]\n",
+            " [   72     0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[24494     3]\n",
+            " [  499     4]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[21402   394]\n",
+            " [ 2564   640]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[24946     0]\n",
+            " [   54     0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[24941     0]\n",
+            " [   59     0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[24673     0]\n",
+            " [  327     0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[24888     0]\n",
+            " [  112     0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[24464     0]\n",
+            " [  536     0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[24716     0]\n",
+            " [  284     0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[24937     0]\n",
+            " [   63     0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[18045   618]\n",
+            " [ 5523   814]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[24690     0]\n",
+            " [  310     0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[14362  2158]\n",
+            " [ 2938  5542]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[24764     0]\n",
+            " [  236     0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[24438     0]\n",
+            " [  562     0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[13923  2293]\n",
+            " [ 3386  5398]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[24421     1]\n",
+            " [  578     0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[19625   724]\n",
+            " [ 3318  1333]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[24176     6]\n",
+            " [  808    10]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[24370     0]\n",
+            " [  630     0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[24969     0]\n",
+            " [   31     0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[24740     0]\n",
+            " [  260     0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[24755     0]\n",
+            " [  245     0]]\n",
+            "\n",
+            "Salines\n",
+            "[[24980     0]\n",
+            " [   20     0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[24447     0]\n",
+            " [  553     0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[21166   430]\n",
+            " [  429  2975]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[24605     0]\n",
+            " [  395     0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[16153  1587]\n",
+            " [ 4931  2329]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[24532     0]\n",
+            " [  468     0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[22054   139]\n",
+            " [ 2473   334]]\n",
+            "\n",
+            "Water courses\n",
+            "[[24516     0]\n",
+            " [  484     0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 9:   0% 0/138 [00:00<?, ?it/s, loss=0.135, v_num=0, loss/train_step=0.128, loss/val_step=0.130, loss/val_epoch=0.129, loss/train_epoch=0.136]/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "Epoch 9:  58% 80/138 [01:00<00:44,  1.31it/s, loss=0.133, v_num=0, loss/train_step=0.129, loss/val_step=0.130, loss/val_epoch=0.129, loss/train_epoch=0.136]\n",
+            "Validation: 0it [00:00, ?it/s]\u001b[A/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "\n",
+            "Validation:   0% 0/40 [00:00<?, ?it/s]\u001b[A\n",
+            "Epoch 9:  72% 100/138 [01:15<00:28,  1.33it/s, loss=0.133, v_num=0, loss/train_step=0.129, loss/val_step=0.130, loss/val_epoch=0.129, loss/train_epoch=0.136]\n",
+            "Epoch 9:  87% 120/138 [01:21<00:12,  1.47it/s, loss=0.133, v_num=0, loss/train_step=0.129, loss/val_step=0.130, loss/val_epoch=0.129, loss/train_epoch=0.136]\n",
+            "Epoch 9: 100% 138/138 [01:28<00:00,  1.56it/s, loss=0.133, v_num=0, loss/train_step=0.129, loss/val_step=0.130, loss/val_epoch=0.129, loss/train_epoch=0.136][2022-10-13 22:51:49,383][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    val Epoch: 9\n",
+            "    val Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.64      0.30      0.41       608\n",
+            "                                                                              Airports       0.00      0.00      0.00         5\n",
+            "                                          Annual crops associated with permanent crops       0.00      0.00      0.00       125\n",
+            "                                                                             Bare rock       0.00      0.00      0.00        24\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00         5\n",
+            "                                                                   Broad-leaved forest       0.60      0.26      0.37      2714\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00         3\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        26\n",
+            "                                                          Complex cultivation patterns       0.70      0.01      0.03      2011\n",
+            "                                                                     Coniferous forest       0.77      0.72      0.74      3116\n",
+            "                                                                    Construction sites       0.00      0.00      0.00         4\n",
+            "                                                               Continuous urban fabric       0.50      0.01      0.02       205\n",
+            "                                                            Discontinuous urban fabric       0.64      0.20      0.30      1288\n",
+            "                                                                            Dump sites       0.00      0.00      0.00         7\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        17\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00        34\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00         9\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       220\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00        90\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        12\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.61      0.09      0.15      2485\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00        24\n",
+            "                                                                          Mixed forest       0.71      0.68      0.69      3378\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00        96\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       202\n",
+            "                                                             Non-irrigated arable land       0.72      0.60      0.65      3525\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       236\n",
+            "                                                                              Pastures       0.66      0.41      0.50      1977\n",
+            "                                                                              Peatbogs       0.00      0.00      0.00       289\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       277\n",
+            "                                                                            Port areas       0.00      0.00      0.00         3\n",
+            "                                                                           Rice fields       0.00      0.00      0.00        28\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00        13\n",
+            "                                                                               Salines       0.00      0.00      0.00         5\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        11\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       207\n",
+            "                                                                         Sea and ocean       0.88      0.91      0.89      1386\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00         8\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00        32\n",
+            "                                                           Transitional woodland/shrub       0.57      0.30      0.40      2828\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       199\n",
+            "                                                                          Water bodies       0.82      0.10      0.17      1096\n",
+            "                                                                         Water courses       0.00      0.00      0.00       173\n",
+            "\n",
+            "                                                                             micro avg       0.71      0.38      0.50     29001\n",
+            "                                                                             macro avg       0.21      0.11      0.12     29001\n",
+            "                                                                          weighted avg       0.63      0.38      0.43     29001\n",
+            "                                                                           samples avg       0.66      0.47      0.51     29001\n",
+            "\n",
+            "    val Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[9288  104]\n",
+            " [ 424  184]]\n",
+            "\n",
+            "Airports\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[9875    0]\n",
+            " [ 125    0]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[6813  473]\n",
+            " [1997  717]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[9974    0]\n",
+            " [  26    0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[7978   11]\n",
+            " [1985   26]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[6200  684]\n",
+            " [ 883 2233]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[9996    0]\n",
+            " [   4    0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[9793    2]\n",
+            " [ 203    2]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[8565  147]\n",
+            " [1031  257]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[9993    0]\n",
+            " [   7    0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[9983    0]\n",
+            " [  17    0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[9966    0]\n",
+            " [  34    0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[9991    0]\n",
+            " [   9    0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[9780    0]\n",
+            " [ 220    0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[9910    0]\n",
+            " [  90    0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[9988    0]\n",
+            " [  12    0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[7380  135]\n",
+            " [2271  214]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[9976    0]\n",
+            " [  24    0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[5674  948]\n",
+            " [1076 2302]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[9904    0]\n",
+            " [  96    0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[9798    0]\n",
+            " [ 202    0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[5655  820]\n",
+            " [1417 2108]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[9764    0]\n",
+            " [ 236    0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[7605  418]\n",
+            " [1174  803]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[9709    2]\n",
+            " [ 289    0]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[9723    0]\n",
+            " [ 277    0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[9997    0]\n",
+            " [   3    0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[9972    0]\n",
+            " [  28    0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[9987    0]\n",
+            " [  13    0]]\n",
+            "\n",
+            "Salines\n",
+            "[[9995    0]\n",
+            " [   5    0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[9989    0]\n",
+            " [  11    0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[9793    0]\n",
+            " [ 207    0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[8443  171]\n",
+            " [ 130 1256]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[9992    0]\n",
+            " [   8    0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[9968    0]\n",
+            " [  32    0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[6535  637]\n",
+            " [1967  861]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[9801    0]\n",
+            " [ 199    0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[8881   23]\n",
+            " [ 990  106]]\n",
+            "\n",
+            "Water courses\n",
+            "[[9827    0]\n",
+            " [ 173    0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 9: 100% 138/138 [01:29<00:00,  1.53it/s, loss=0.133, v_num=0, loss/train_step=0.131, loss/val_step=0.129, loss/val_epoch=0.127, loss/train_epoch=0.136]\n",
+            "                                                            \u001b[A[2022-10-13 22:51:51,341][bigearthnet.models.bigearthnet_module][INFO] - \n",
+            "    train Epoch: 9\n",
+            "    train Classification report:\n",
+            "                                                                                        precision    recall  f1-score   support\n",
+            "\n",
+            "                                                                   Agro-forestry areas       0.67      0.27      0.38      1474\n",
+            "                                                                              Airports       0.00      0.00      0.00        60\n",
+            "                                          Annual crops associated with permanent crops       0.50      0.00      0.01       356\n",
+            "                                                                             Bare rock       0.00      0.00      0.00       193\n",
+            "                                                                 Beaches, dunes, sands       0.00      0.00      0.00       104\n",
+            "                                                                   Broad-leaved forest       0.66      0.24      0.35      6897\n",
+            "                                                                           Burnt areas       0.00      0.00      0.00        19\n",
+            "                                                                       Coastal lagoons       0.00      0.00      0.00        83\n",
+            "                                                          Complex cultivation patterns       0.59      0.09      0.16      4932\n",
+            "                                                                     Coniferous forest       0.77      0.72      0.74      8108\n",
+            "                                                                    Construction sites       0.00      0.00      0.00        72\n",
+            "                                                               Continuous urban fabric       0.67      0.01      0.02       503\n",
+            "                                                            Discontinuous urban fabric       0.63      0.23      0.33      3204\n",
+            "                                                                            Dump sites       0.00      0.00      0.00        54\n",
+            "                                                                             Estuaries       0.00      0.00      0.00        59\n",
+            "                                                     Fruit trees and berry plantations       0.00      0.00      0.00       327\n",
+            "                                                                     Green urban areas       0.00      0.00      0.00       112\n",
+            "                                                        Industrial or commercial units       0.00      0.00      0.00       536\n",
+            "                                                                        Inland marshes       0.00      0.00      0.00       284\n",
+            "                                                                      Intertidal flats       0.00      0.00      0.00        63\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation       0.57      0.15      0.23      6337\n",
+            "                                                              Mineral extraction sites       0.00      0.00      0.00       310\n",
+            "                                                                          Mixed forest       0.71      0.66      0.68      8480\n",
+            "                                                                   Moors and heathland       0.00      0.00      0.00       236\n",
+            "                                                                     Natural grassland       0.00      0.00      0.00       562\n",
+            "                                                             Non-irrigated arable land       0.71      0.63      0.67      8784\n",
+            "                                                                          Olive groves       0.00      0.00      0.00       578\n",
+            "                                                                              Pastures       0.65      0.30      0.41      4651\n",
+            "                                                                              Peatbogs       0.65      0.02      0.03       818\n",
+            "                                                            Permanently irrigated land       0.00      0.00      0.00       630\n",
+            "                                                                            Port areas       0.00      0.00      0.00        31\n",
+            "                                                                           Rice fields       0.00      0.00      0.00       260\n",
+            "                                            Road and rail networks and associated land       0.00      0.00      0.00       245\n",
+            "                                                                               Salines       0.00      0.00      0.00        20\n",
+            "                                                                          Salt marshes       0.00      0.00      0.00        82\n",
+            "                                                             Sclerophyllous vegetation       0.00      0.00      0.00       553\n",
+            "                                                                         Sea and ocean       0.88      0.88      0.88      3404\n",
+            "                                                              Sparsely vegetated areas       0.00      0.00      0.00        82\n",
+            "                                                          Sport and leisure facilities       0.00      0.00      0.00       395\n",
+            "                                                           Transitional woodland/shrub       0.59      0.33      0.42      7260\n",
+            "                                                                             Vineyards       0.00      0.00      0.00       468\n",
+            "                                                                          Water bodies       0.71      0.16      0.26      2807\n",
+            "                                                                         Water courses       0.00      0.00      0.00       484\n",
+            "\n",
+            "                                                                             micro avg       0.71      0.38      0.49     74917\n",
+            "                                                                             macro avg       0.23      0.11      0.13     74917\n",
+            "                                                                          weighted avg       0.61      0.38      0.44     74917\n",
+            "                                                                           samples avg       0.66      0.46      0.51     74917\n",
+            "\n",
+            "    train Confusion matrices:\n",
+            "\n",
+            "Agro-forestry areas\n",
+            "[[23336   190]\n",
+            " [ 1082   392]]\n",
+            "\n",
+            "Airports\n",
+            "[[24940     0]\n",
+            " [   60     0]]\n",
+            "\n",
+            "Annual crops associated with permanent crops\n",
+            "[[24643     1]\n",
+            " [  355     1]]\n",
+            "\n",
+            "Bare rock\n",
+            "[[24807     0]\n",
+            " [  193     0]]\n",
+            "\n",
+            "Beaches, dunes, sands\n",
+            "[[24896     0]\n",
+            " [  104     0]]\n",
+            "\n",
+            "Broad-leaved forest\n",
+            "[[17246   857]\n",
+            " [ 5226  1671]]\n",
+            "\n",
+            "Burnt areas\n",
+            "[[24981     0]\n",
+            " [   19     0]]\n",
+            "\n",
+            "Coastal lagoons\n",
+            "[[24917     0]\n",
+            " [   83     0]]\n",
+            "\n",
+            "Complex cultivation patterns\n",
+            "[[19754   314]\n",
+            " [ 4485   447]]\n",
+            "\n",
+            "Coniferous forest\n",
+            "[[15184  1708]\n",
+            " [ 2291  5817]]\n",
+            "\n",
+            "Construction sites\n",
+            "[[24928     0]\n",
+            " [   72     0]]\n",
+            "\n",
+            "Continuous urban fabric\n",
+            "[[24495     2]\n",
+            " [  499     4]]\n",
+            "\n",
+            "Discontinuous urban fabric\n",
+            "[[21377   419]\n",
+            " [ 2482   722]]\n",
+            "\n",
+            "Dump sites\n",
+            "[[24946     0]\n",
+            " [   54     0]]\n",
+            "\n",
+            "Estuaries\n",
+            "[[24941     0]\n",
+            " [   59     0]]\n",
+            "\n",
+            "Fruit trees and berry plantations\n",
+            "[[24673     0]\n",
+            " [  327     0]]\n",
+            "\n",
+            "Green urban areas\n",
+            "[[24888     0]\n",
+            " [  112     0]]\n",
+            "\n",
+            "Industrial or commercial units\n",
+            "[[24464     0]\n",
+            " [  536     0]]\n",
+            "\n",
+            "Inland marshes\n",
+            "[[24716     0]\n",
+            " [  284     0]]\n",
+            "\n",
+            "Intertidal flats\n",
+            "[[24937     0]\n",
+            " [   63     0]]\n",
+            "\n",
+            "Land principally occupied by agriculture, with significant areas of natural vegetation\n",
+            "[[17981   682]\n",
+            " [ 5416   921]]\n",
+            "\n",
+            "Mineral extraction sites\n",
+            "[[24690     0]\n",
+            " [  310     0]]\n",
+            "\n",
+            "Mixed forest\n",
+            "[[14300  2220]\n",
+            " [ 2920  5560]]\n",
+            "\n",
+            "Moors and heathland\n",
+            "[[24764     0]\n",
+            " [  236     0]]\n",
+            "\n",
+            "Natural grassland\n",
+            "[[24438     0]\n",
+            " [  562     0]]\n",
+            "\n",
+            "Non-irrigated arable land\n",
+            "[[13947  2269]\n",
+            " [ 3275  5509]]\n",
+            "\n",
+            "Olive groves\n",
+            "[[24422     0]\n",
+            " [  578     0]]\n",
+            "\n",
+            "Pastures\n",
+            "[[19606   743]\n",
+            " [ 3272  1379]]\n",
+            "\n",
+            "Peatbogs\n",
+            "[[24175     7]\n",
+            " [  805    13]]\n",
+            "\n",
+            "Permanently irrigated land\n",
+            "[[24370     0]\n",
+            " [  630     0]]\n",
+            "\n",
+            "Port areas\n",
+            "[[24969     0]\n",
+            " [   31     0]]\n",
+            "\n",
+            "Rice fields\n",
+            "[[24740     0]\n",
+            " [  260     0]]\n",
+            "\n",
+            "Road and rail networks and associated land\n",
+            "[[24755     0]\n",
+            " [  245     0]]\n",
+            "\n",
+            "Salines\n",
+            "[[24980     0]\n",
+            " [   20     0]]\n",
+            "\n",
+            "Salt marshes\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sclerophyllous vegetation\n",
+            "[[24447     0]\n",
+            " [  553     0]]\n",
+            "\n",
+            "Sea and ocean\n",
+            "[[21172   424]\n",
+            " [  423  2981]]\n",
+            "\n",
+            "Sparsely vegetated areas\n",
+            "[[24918     0]\n",
+            " [   82     0]]\n",
+            "\n",
+            "Sport and leisure facilities\n",
+            "[[24605     0]\n",
+            " [  395     0]]\n",
+            "\n",
+            "Transitional woodland/shrub\n",
+            "[[16124  1616]\n",
+            " [ 4890  2370]]\n",
+            "\n",
+            "Vineyards\n",
+            "[[24532     0]\n",
+            " [  468     0]]\n",
+            "\n",
+            "Water bodies\n",
+            "[[22016   177]\n",
+            " [ 2363   444]]\n",
+            "\n",
+            "Water courses\n",
+            "[[24516     0]\n",
+            " [  484     0]]\n",
+            "\n",
+            "    \n",
+            "Epoch 9: 100% 138/138 [01:30<00:00,  1.53it/s, loss=0.133, v_num=0, loss/train_step=0.131, loss/val_step=0.129, loss/val_epoch=0.127, loss/train_epoch=0.134]`Trainer.fit` stopped: `max_epochs=10` reached.\n",
+            "Epoch 9: 100% 138/138 [01:30<00:00,  1.52it/s, loss=0.133, v_num=0, loss/train_step=0.131, loss/val_step=0.129, loss/val_epoch=0.127, loss/train_epoch=0.134]\n",
+            "FIT Profiler Report\n",
+            "Profile stats for: records\n",
+            "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
+            "                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg    # of Calls  \n",
+            "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
+            "                                          ProfilerStep*         7.11%      42.391ms        95.60%     570.159ms     285.079ms       0.000us         0.00%     109.512ms      54.756ms             2  \n",
+            "[pl][profile][EvaluationEpochLoop].val_dataloader_id...         0.17%     991.000us        70.49%     420.378ms     210.189ms       0.000us         0.00%       0.000us       0.000us             2  \n",
+            "enumerate(DataLoader)#_MultiProcessingDataLoaderIter...        70.31%     419.292ms        70.32%     419.366ms     209.683ms       0.000us         0.00%       0.000us       0.000us             2  \n",
+            "                                        cudaMemcpyAsync        11.97%      71.357ms        11.97%      71.357ms       5.097ms       0.000us         0.00%       0.000us       0.000us            14  \n",
+            "[pl][profile][Strategy]SingleDeviceStrategy.batch_to...         1.14%       6.799ms         9.39%      55.984ms      27.992ms       0.000us         0.00%      44.657ms      22.328ms             2  \n",
+            "[pl][profile][LightningModule]BigEarthNetModule.tran...         0.05%     328.000us         8.22%      49.039ms      24.520ms       0.000us         0.00%      44.657ms      22.328ms             2  \n",
+            "                                               aten::to         0.00%      24.000us         8.19%      48.833ms       3.488ms       0.000us         0.00%      44.665ms       3.190ms            14  \n",
+            "                                         aten::_to_copy         0.01%      55.000us         8.18%      48.809ms       8.135ms       0.000us         0.00%      44.665ms       7.444ms             6  \n",
+            "                                            aten::copy_         0.03%     181.000us         8.18%      48.786ms       4.879ms      44.673ms        40.79%      44.673ms       4.467ms            10  \n",
+            "[pl][profile][Strategy]SingleDeviceStrategy.validati...         0.22%       1.292ms         4.46%      26.612ms      13.306ms       0.000us         0.00%      64.848ms      32.424ms             2  \n",
+            "                                  cudaDeviceSynchronize         4.39%      26.193ms         4.39%      26.193ms      26.193ms       0.000us         0.00%       0.000us       0.000us             1  \n",
+            "                                             aten::item         0.00%      19.000us         3.84%      22.929ms       3.821ms       0.000us         0.00%       7.000us       1.167us             6  \n",
+            "                              aten::_local_scalar_dense         0.01%      82.000us         3.84%      22.910ms       3.818ms       7.000us         0.01%       7.000us       1.167us             6  \n",
+            "[pl][module]bigearthnet.models.nets.baseline.Baselin...         0.04%     212.000us         2.77%      16.548ms       8.274ms       0.000us         0.00%      64.752ms      32.376ms             2  \n",
+            "[pl][module]torch.nn.modules.container.Sequential: m...         0.11%     680.000us         2.48%      14.786ms       7.393ms       0.000us         0.00%      64.021ms      32.011ms             2  \n",
+            "                                           aten::conv2d         0.00%      24.000us         2.08%      12.423ms       2.071ms       0.000us         0.00%      43.353ms       7.226ms             6  \n",
+            "                                aten::_convolution_mode         0.01%      33.000us         2.08%      12.399ms       2.067ms       0.000us         0.00%      43.353ms       7.226ms             6  \n",
+            "                                     aten::_convolution         0.40%       2.388ms         2.07%      12.366ms       2.061ms       0.000us         0.00%      43.353ms       7.226ms             6  \n",
+            "                                aten::cudnn_convolution         1.46%       8.719ms         1.62%       9.658ms       1.610ms      32.372ms        29.56%      32.372ms       5.395ms             6  \n",
+            "[pl][module]torch.nn.modules.conv.Conv2d: model.conv...         0.02%     116.000us         1.47%       8.760ms       4.380ms       0.000us         0.00%      22.460ms      11.230ms             2  \n",
+            "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
+            "Self CPU time total: 596.379ms\n",
+            "Self CUDA time total: 109.512ms\n",
+            "\n",
+            "[2022-10-13 22:51:52,173][__main__][INFO] - Training Done.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We can also train on e.g. a vision transformer using TIMM pretrained models. Note that you will need to make sure your GPU has enough memory!\n",
+        "\n",
+        "    !python train.py model=timm ++model.model_name=vit_base_patch16_224 ++model.pretrained=true ++datamodule.dataset_name='bigearthnet-medium' ++datamodule.num_workers=2 ++datamodule.batch_size=256 ++trainer.max_epochs=10 +trainer.accelerator='gpu' +trainer.devices=1\n"
+      ],
+      "metadata": {
+        "id": "leO17ye8Yiv2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Model Evaluation\n",
+        "\n",
+        "Here we will get a pre-trained model (either a ViT or a ConvNext), and evaluate it on the medium test set."
+      ],
+      "metadata": {
+        "id": "iVED0PytQzBY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Download the model\n",
+        "\n",
+        "import gdown\n",
+        "\n",
+        "pretrained_models = {\n",
+        "    \"vit\": \"https://drive.google.com/file/d/1uVPSDAaDnEUDoa4fCshHXgwYORaeSLt6/view?usp=sharing\",\n",
+        "    \"convnext\": \"https://drive.google.com/file/d/1EyLDVoZKK-GZNnr_VEHNoH9eJoVbi2Nx/view?usp=sharing\",\n",
+        "}\n",
+        "\n",
+        "model_name = 'vit'\n",
+        "gdown.download(pretrained_models[model_name], \"best-model.ckpt\", fuzzy=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 107
+        },
+        "id": "moREBpx6Q56G",
+        "outputId": "9b3f1429-abf3-4aae-a956-871ed45b4eaf"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1uVPSDAaDnEUDoa4fCshHXgwYORaeSLt6\n",
+            "To: /content/best-model.ckpt\n",
+            "100%|██████████| 1.03G/1.03G [00:06<00:00, 168MB/s]\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'best-model.ckpt'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Evaluate the model on bigearthnet-medium\n",
+        "\n",
+        "!python eval.py --ckpt-path best-model.ckpt --dataset-name bigearthnet-medium --accelerator 'gpu' --devices 1"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dWbcBRedR2fZ",
+        "outputId": "b281e672-afb2-4840-f2cc-1543116e3085"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "../datasets/bigearthnet-medium/train loaded successfully.\n",
+            "../datasets/bigearthnet-medium/val loaded successfully.\n",
+            "../datasets/bigearthnet-medium/test loaded successfully.\n",
+            "GPU available: True (cuda), used: True\n",
+            "TPU available: False, using: 0 TPU cores\n",
+            "IPU available: False, using: 0 IPUs\n",
+            "HPU available: False, using: 0 HPUs\n",
+            "Missing logger folder: /content/bigearthnet/bigearthnet/lightning_logs\n",
+            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+            "Testing: 0it [00:00, ?it/s]/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n",
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "Testing DataLoader 0: 100% 157/157 [02:02<00:00,  1.28it/s]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from PIL import Image\n",
+        "im = Image.open('test_conf_mats.png')\n",
+        "display(im)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 885
+        },
+        "id": "u0jWE3WkSXoE",
+        "outputId": "a787955f-7bbd-4f70-8f17-72aafc3b4ffe"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1200x1500 at 0x7F67246E2210>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Individual predictions\n",
+        "from bigearthnet.datamodules.bigearthnet_datamodule import BigEarthNetDataModule\n",
+        "\n",
+        "dataset_dir = \"../datasets/\" # root directory where to download the datasets\n",
+        "dataset_name = \"bigearthnet-medium\" # One of bigearthnet-mini, bigearthnet-medium, bigearthnet-full\n",
+        "batch_size = 16\n",
+        "\n",
+        "dm = BigEarthNetDataModule(dataset_dir, dataset_name, batch_size=batch_size)\n",
+        "dm.setup()\n",
+        "ds = dm.test_dataloader().dataset"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vYBjdxGPTtlQ",
+        "outputId": "b349d806-8ad7-49be-ad1e-2e08f370de12"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1YW4ugRQTl-YF_ZpLO7gIRSlHnB2Cwslz\n",
+            "To: /datasets/bigearthnet-medium.tar\n",
+            "100%|██████████| 2.72G/2.72G [00:18<00:00, 145MB/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "../datasets/bigearthnet-medium/train loaded successfully.\n",
+            "../datasets/bigearthnet-medium/val loaded successfully.\n",
+            "../datasets/bigearthnet-medium/test loaded successfully.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Model Predictions\n",
+        "\n",
+        "Here let's look at how to make individual predictions using our model"
+      ],
+      "metadata": {
+        "id": "FaA-0gMTaepW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from bigearthnet.models.bigearthnet_module import BigEarthNetModule\n",
+        "from bigearthnet.datamodules.bigearthnet_datamodule import BigEarthNetDataModule\n",
+        "from hydra.utils import instantiate\n",
+        "\n",
+        "ckpt_path = 'best-model.ckpt'\n",
+        "dataset_dir = \"../datasets/\" # root directory where to download the datasets\n",
+        "dataset_name = \"bigearthnet-medium\" # One of bigearthnet-mini, bigearthnet-medium, bigearthnet-full\n",
+        "batch_size = 16\n",
+        "num_workers = 0\n",
+        "\n",
+        "\n",
+        "# Load the model from the checkpoint\n",
+        "model = BigEarthNetModule.load_from_checkpoint(ckpt_path)\n",
+        "\n",
+        "# fetch the transforms used in the model\n",
+        "transforms = instantiate(model.cfg.transforms.obj)\n",
+        "\n",
+        "datamodule = BigEarthNetDataModule(\n",
+        "    dataset_dir, dataset_name, batch_size, num_workers, transforms\n",
+        ")\n",
+        "datamodule.setup()\n",
+        "ds = datamodule.test_dataloader().dataset\n",
+        "\n",
+        "# Load without transforms so we can see our images too\n",
+        "datamodule_no_tfm = BigEarthNetDataModule(\n",
+        "    dataset_dir, dataset_name, batch_size, num_workers, transforms=None\n",
+        ")\n",
+        "datamodule_no_tfm.setup()\n",
+        "ds_no_tfm = datamodule_no_tfm.test_dataloader().dataset"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "z3sSSdBNUgJH",
+        "outputId": "78c535d6-5cfa-4aca-e36d-82a17d73dc12"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "../datasets/bigearthnet-medium/train loaded successfully.\n",
+            "../datasets/bigearthnet-medium/val loaded successfully.\n",
+            "../datasets/bigearthnet-medium/test loaded successfully.\n",
+            "../datasets/bigearthnet-medium/train loaded successfully.\n",
+            "../datasets/bigearthnet-medium/val loaded successfully.\n",
+            "../datasets/bigearthnet-medium/test loaded successfully.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Displays image and ground truth\n",
+        "sample_index = 0\n",
+        "\n",
+        "_ = display_image(ds_no_tfm, idx=sample_index)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 309
+        },
+        "id": "ncBS8nHEXRb_",
+        "outputId": "f520bab9-f0e3-4f66-da35-1f63e19da991"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "# fetch the data and reshape the input for our model\n",
+        "data = torch.unsqueeze(ds[sample_index]['data'], dim=0) \n",
+        "logits = model.model(data)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NGMXlxC5XUwd",
+        "outputId": "d00c00d4-6886-43a0-f276-7a5f3db15f4d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/deeplake/core/dataset/dataset.py:447: UserWarning: Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\n",
+            "  \"Indexing by integer in a for loop, like `for i in range(len(ds)): ... ds[i]` can be quite slow. Use `for i, sample in enumerate(ds)` instead.\"\n",
+            "/content/bigearthnet/bigearthnet/datamodules/bigearthnet_datamodule.py:58: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+            "  onehot_labels[[hub_labels]] = 1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Predictions:\\n\")\n",
+        "preds = torch.sigmoid(logits) > 0.5\n",
+        "indices = torch.where(preds)[1] # get only positive predictions\n",
+        "for idx in indices: # iterate through the prediction indices\n",
+        "    print(ds.class_names[idx])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1sUIGgleZiw-",
+        "outputId": "f0f4a055-379b-4668-9c16-ee34b1c71b12"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predictions:\n",
+            "\n",
+            "Pastures\n",
+            "Peatbogs\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "preds = torch.sigmoid(logits) > 0.2\n",
+        "indices = torch.where(preds)[1]\n",
+        "print(\"Predictions:\\n\")\n",
+        "for idx in indices:\n",
+        "    print(ds.class_names[idx])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VTH2-keZYTBa",
+        "outputId": "cab4cc3c-f873-4436-82a4-2de387cc1eab"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predictions:\n",
+            "\n",
+            "Coniferous forest\n",
+            "Moors and heathland\n",
+            "Pastures\n",
+            "Peatbogs\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file