RandSpatialCrop(random_size=False) does not crop in lazy mode

[PaulJehanno]

Describe the bug
As title says : RandSpatialCropd transform does not seem to crop when in lazy mode.

To Reproduce

import torch
from torchvision.transforms import Compose
import monai.transforms

x = torch.rand((1, 10, 10, 10))
t = monai.transforms.RandSpatialCrop((5, 5, 5), random_size=False, lazy=True)
t(x).size()
# returns 10,10,10 for all

Expected behavior
should return 5,5,5

Environment

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Printing MONAI config...
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MONAI version: 1.3.dev2329
Numpy version: 1.24.2
Pytorch version: 1.13.1+cu117
MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False
MONAI rev id: c82c65a288e396f8f7d059e82373edb1814b20be
MONAI __file__: /home/ec2-user/.pyenv/versions/3.9.2/lib/python3.9/site-packages/monai/__init__.py

Optional dependencies:
Pytorch Ignite version: NOT INSTALLED or UNKNOWN VERSION.
ITK version: NOT INSTALLED or UNKNOWN VERSION.
Nibabel version: 5.0.1
scikit-image version: 0.20.0
Pillow version: 9.4.0
Tensorboard version: NOT INSTALLED or UNKNOWN VERSION.
gdown version: NOT INSTALLED or UNKNOWN VERSION.
TorchVision version: 0.14.1+cu117
tqdm version: 4.64.1
lmdb version: NOT INSTALLED or UNKNOWN VERSION.
psutil version: NOT INSTALLED or UNKNOWN VERSION.
pandas version: 1.5.3
einops version: NOT INSTALLED or UNKNOWN VERSION.
transformers version: NOT INSTALLED or UNKNOWN VERSION.
mlflow version: NOT INSTALLED or UNKNOWN VERSION.
pynrrd version: NOT INSTALLED or UNKNOWN VERSION.

For details about installing the optional dependencies, please visit:
    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies


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Printing system config...
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`psutil` required for `print_system_info`

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Printing GPU config...
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Num GPUs: 0
Has CUDA: False
cuDNN enabled: True
cuDNN version: 8500

[wyli]

thanks, this is mainly a usage question, the pending operations could be applied with:

import torch
from torchvision.transforms import Compose
import monai.transforms
from monai.transforms.lazy.functional import apply_pending_transforms

x = torch.rand((1, 10, 10, 10))
t = monai.transforms.RandSpatialCrop((5, 5, 5), random_size=False, lazy=True)
out = t(x)
print(apply_pending_transforms(out, None, logger_name=True).size())

please see https://github.com/Project-MONAI/tutorials/blob/main/modules/lazy_resampling_functional.ipynb