Updated Code Snippet for PyTorch tutorial for "example_pytorch_data_generation.ipynb"

[SumaiyaTarannumNoor]

Pull Request Description:

Previous Code Snippet:

run post training quantization on the model to get the quantized model output

quantized_model_generated_data, quantization_info = mct.ptq.pytorch_post_training_quantization(
in_module=float_model,
representative_data_gen=representative_data_gen,
target_platform_capabilities=target_platform_cap
)
This code snippet is unable to detect the float_model and it's parameters. While using all the same snippets from "example_pytorch_data_generation.ipynb" in here - https://github.com/sony/model_optimization/blob/main/tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_data_generation.ipynb
I was given this runtime error in Google Colab.
RuntimeError: Only Tensors created explicitly by the user (graph leaves) support the deepcopy protocol at the moment. If you were attempting to deepcopy a module, this may be because of a torch.nn.utils.weight_norm usage, see pytorch/pytorch#103001

However, I have updated this code snippets, which will make a new float_model with the same architecture and using it for Quantization. This is working perfectly.

Checklist before requesting a review:

• I set the appropriate labels on the pull request.
• I have added/updated the release note draft (if necessary).
• I have updated the documentation to reflect my changes (if necessary).
• All function and files are well documented.
• All function and classes have type hints.
• There is a licenses in all file.
• The function and variable names are informative.
• I have checked for code duplications.
• I have added new unittest (if necessary).