diff --git a/docs/export/CPP_STM32.rst b/docs/export/CPP_STM32.rst
index 347818ca..f56e9d71 100644
--- a/docs/export/CPP_STM32.rst
+++ b/docs/export/CPP_STM32.rst
@@ -1,8 +1,6 @@
 Export: C++/STM32
 =================
 
-**N2D2-IP only: available upon request.**
-
 Export type: ``CPP_STM32``
  C++ export for STM32.
 
diff --git a/docs/quant/pruning.rst b/docs/quant/pruning.rst
index 204593f7..2e342eab 100644
--- a/docs/quant/pruning.rst
+++ b/docs/quant/pruning.rst
@@ -15,16 +15,16 @@ Example with Python
         :members:
         :inherited-members:
 
-Example of code to use the *PruneCell* in your scripts:
+Example of code to use the :py:class:`n2d2.quantizer.PruneCell` in your scripts:
 
 .. code-block:: python
 
     for cell in model:
-    ### Add Pruning ###
-    if isinstance(cell, n2d2.cells.Conv) or isinstance(cell, n2d2.cells.Fc):
-        cell.quantizer = n2d2.quantizer.PruneCell(prune_mode="Static", threshold=0.3, prune_filler="IterNonStruct")
+        ### Add Pruning ###
+        if isinstance(cell, n2d2.cells.Conv) or isinstance(cell, n2d2.cells.Fc):
+            cell.quantizer = n2d2.quantizer.PruneCell(prune_mode="Static", threshold=0.3, prune_filler="IterNonStruct")
 
-Some explanations with the differents options of the *PruneCell*:
+Some explanations with the differents options of the :py:class:`n2d2.quantizer.PruneCell` :
 
 Pruning mode
 ^^^^^^^^^^^^
@@ -42,7 +42,7 @@ For example, to update each two epochs, write:
 
     n2d2.quantizer.PruneCell(prune_mode="Gradual", threshold=0.3, stepsize=2*DATASET_SIZE)
 
-Where *DATASET_SIZE* is the size of the dataset you are using.
+Where ``DATASET_SIZE`` is the size of the dataset you are using.
 
 Pruning filler
 ^^^^^^^^^^^^^^
@@ -53,7 +53,7 @@ Pruning filler
 - IterNonStruct: all weights below than the ``delta`` factor are pruned. If this is not enough to reach ``threshold``, all the weights below 2 "delta" are pruned and so on...
 
 
-**Important**: With *PruneCell*, ``quant_mode`` and ``range`` are not used.
+**Important**: With :py:class:`n2d2.quantizer.PruneCell`, ``quant_mode`` and ``range`` are not used.
 
 
 Example with INI file
diff --git a/python/pytorch_to_n2d2/pytorch_interface.py b/python/pytorch_to_n2d2/pytorch_interface.py
index af6df233..332e5920 100755
--- a/python/pytorch_to_n2d2/pytorch_interface.py
+++ b/python/pytorch_to_n2d2/pytorch_interface.py
@@ -331,6 +331,8 @@ def __exit__(self, exc_type, exc_value, traceback):
 def wrap(torch_model:torch.nn.Module,
         input_size: Union[list, tuple],
         opset_version:int=11,
+        in_names:list=None,
+        out_names:list=None,
         verbose:bool=False) -> Block:
     """Function generating a ``torch.nn.Module`` which embed a :py:class:`n2d2.cells.DeepNetCell`.
     The torch_model is exported to N2D2 via ONNX.
@@ -341,6 +343,10 @@ def wrap(torch_model:torch.nn.Module,
     :type input_size: ``list``
     :param opset_version: Opset version used to generate the intermediate ONNX file, default=11
     :type opset_version: int, optional
+    :param in_names: Specify specific names for the network inputs
+    :type in_names: list, optional
+    :param out_names: Specify specific names for the network outputs
+    :type in_names: list, optional
     :param verbose: Enable the verbose output of torch onnx export, default=False
     :type verbose: bool, optional
     :return: A custom ``torch.nn.Module`` which embed a :py:class:`n2d2.cells.DeepNetCell`.
@@ -361,6 +367,8 @@ def wrap(torch_model:torch.nn.Module,
             dummy_in,
             raw_model_path,
             verbose=verbose,
+            input_names=in_names,
+            output_names=out_names,
             export_params=True,
             opset_version=opset_version,
             training=torch.onnx.TrainingMode.TRAINING,