Bug fix for named nn.Sequential in pytorch parser

Created by: JanFSchulte

Parsing of nn.Sequentials that are named members of a model class results in a naming convention for the tensors in the state_dict of the model different from what the parser expects, since it was so far tested only on unnamed nn.Sequentials. This PR catches this and adjusts the name of the tensors we are importing from the state_dict accordingly. A test is added to ensure that we keep parsing both cases successfully.

Type of change

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Note: Please delete options that are not relevant.

• Bug fix (non-breaking change that fixes an issue)

Tests

To reproduce, this will fail with this PR:

import torch.nn as nn

from hls4ml.converters import convert_from_pytorch_model
from hls4ml.utils.config import config_from_pytorch_model



#simple model with namend sequential
class SeqModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.layer = nn.Sequential(
          nn.Conv2d(1,20,5),
          nn.ReLU(),
          nn.Conv2d(20,64,5),
          nn.ReLU()
        )   

    def forward(self, x):
        output = self.layer(x)
        return output

model = SeqModel()

config = config_from_pytorch_model(model)
output_dir = 'test_pytorch'

convert_from_pytorch_model(
        model, (None, 1, 5, 5), hls_config=config, output_dir=output_dir)

pytests have been added to verify that this keeps working.

Checklist

• I have read the guidelines for contributing.
• I have commented my code, particularly in hard-to-understand areas.
• My changes generate no new warnings.
• I have installed and run pre-commit on the files I edited or added.
• I have added tests that prove my fix is effective or that my feature works.