code: def __init__(self):
      super().__init__()
      self.conv1 = nn.Conv1d(
          in_channels=3, out_channels=6, kernel_size=3, bias=False
      )
      self.relu1 = nn.ReLU()
      self.conv2 = nn.Conv1d(
          in_channels=6, out_channels=5, kernel_size=3, bias=False
      )
      self.relu2 = nn.ReLU()
      self.dropout1 = nn.Dropout()  # Should be skipped
      self.flatten = nn.Flatten()
      self.fc1 = nn.Linear(
          in_features=5 * 4, out_features=6, bias=False
      )
      self.dropout2 = nn.Dropout()  # Should be skipped
      self.fc2 = nn.Linear(in_features=6, out_features=5, bias=False)
  
imports: import math
  from pathlib import Path
  import numpy
  import pytest
  import torch
  import torch.nn
  from torch.nn import AvgPool1d, AvgPool2d, MaxPool1d, MaxPool2d
  from hls4ml.converters import convert_from_pytorch_model
  from hls4ml.utils.config import config_from_pytorch_model
label_desc: Use image node_id 1 to create a new ModelSkippedLayers object from inherited base classes: nn with example: obj = ModelSkippedLayers()