code: def __init__(self, n_channels_list: Sequence[int]):
      super(LinLayers, self).__init__(
          [
              nn.Sequential(
                  nn.Identity(), nn.Conv2d(nc, 1, 1, 1, 0, bias=False)
              )
              for nc in n_channels_list
          ]
      )
  
      for param in self.parameters():
          param.requires_grad = False
  
imports: from typing import Sequence
  from itertools import chain
  import torch
  import torch.nn
  from torchvision import models
  from .utils import normalize_activation
label_desc: Use image node_id 1 to create a new LinLayers object from inherited base classes: nn with example: obj = LinLayers(n_channels_list)