class_docstr: A single linear layer which does a 1x1 conv.
class_docstr_tok: ["A","single","linear","layer","which","does","a","1x1","conv","."]
code: def __init__(
      self, chn_in: int, chn_out: int = 1, use_dropout: bool = False
  ) -> None:
      super().__init__()
  
      layers = [nn.Dropout()] if use_dropout else []
      layers += [
          nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),  # type: ignore[list-item]
      ]
      self.model = nn.Sequential(*layers)
  
imports: import inspect
  import os
  from typing import List, NamedTuple, Optional, Tuple, Union
  import torch
  from torch import Tensor, nn
  from typing_extensions import Literal
  from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE, _TORCHVISION_GREATER_EQUAL_0_13
label_desc: Use image node_id 1 to create a new NetLinLayer object from inherited base classes: nn with example: obj = NetLinLayer(chn_in, chn_out, use_dropout)