Feature/472 create model abstraction for declearn integration

MR description

Implements model abstraction for pytorch and scikit learn Closes #472 (closed).

Here a list of points that i found difficult to implement (and that need a closer look).

1. BaseSklearnModel:

• batch_size computation / reset that is done internally
• sklearn model n_iter attribute increment/decrement (hidden through methods)
• apply_updates method: slightly modified from the one of the poc, that adds gradients instead of changing them, in the same spirit as the apply_updates of pytorch. Check if computation is correct
• Toolbox classes, that implement some method using multiple inheritance

2. TorchModel

• method train not implemented: should we compute loss in this method ?
• is get_gradients method correct?
• saving state of the TorchModel: initial parameters are saved in a specific attribute:
• make sure it handles frozen layers

More broadly speaking:

• appropriate raise of exceptions: some exceptions may not be caught
• appropriate naming of variables
• create multiple modules for each classes (ie one class per file) rather than having 3 classes in a file.

Developer Certificate Of Origin (DCO)

By opening this merge request, you agree the Developer Certificate of Origin (DCO)

This DCO essentially means that:

• you offer the changes under the same license agreement as the project, and
• you have the right to do that,
• you did not steal somebody else’s work.

License

Project code files should begin with these comment lines to help trace their origin:

# This file is originally part of Fed-BioMed
# SPDX-License-Identifier: Apache-2.0

Code files can be reused from another project with a compatible non-contaminating license. They shall retain the original license and copyright mentions. The CREDIT.md file and credit/ directory shall be completed and updated accordingly.

Guidelines for MR review

General:

• give a glance to DoD
• check coding rules and coding style
• check docstrings (eg run tests/docstrings/check_docstrings)

Specific to some cases:

• update all conda envs consistently (development and vpn, Linux and MacOS)
• if modified researcher (eg new attributes in classes) check if breakpoint needs update (breakpoint/load_breakpoint in Experiment(), save_state/load_state in aggregators, strategies, secagg, etc.)