Provide default mapping function for trivial pytorch functions from `torch.nn`

[bvogginger]

Description

Currently, the conversion to/from nir of the following pytorch function is handled individually in each pytorch-based framework:

• torch.nn.Conv2d
• torch.nn.Linear

In the future this might apply to other functions like:

• torch.nn.Conv1d and other convolution operations
• torch.nn.AvgPool2d and other average pooling operations
• torch.nn.MaxPool2d and other max pooling operations

This is related to this issue in snntorch: jeshraghian/snntorch#304

Suggestion

To reduce the amount of redundant code, we could implement default mapper functions for these pytorch native operations. This default mapper function could be applied to a nn.Module ore nir.node when the framework specific mapper function does not supply the operation.

Changes for conversion from pytorch to NIR

The suggestion is to implement a default mapper function like _extract_default_model
and call it if there is no NIRNode found by the framework dependent model_map function provided: https://github.com/neuromorphs/NIRTorch/blob/main/nirtorch/to_nir.py#L70

Changes for conversion from NIR to pytorch

Extend function _switch_default_models in https://github.com/neuromorphs/NIRTorch/blob/main/nirtorch/from_nir.py#L188

[bvogginger]

I'm working on this issue.

[Jegp]

After porting to torch.fx there's a need to revisit this; we need default mappings from NIR to Torch (https://github.com/neuromorphs/NIRTorch/blob/main/nirtorch/nir_interpreter.py#L60) and from Torch to NIR (https://github.com/neuromorphs/NIRTorch/blob/main/nirtorch/torch_tracer.py#L63)