code: def __init__(self, in_channels: int, use_dropout: bool):
      super().__init__()
      blocks = []
      if use_dropout:
          blocks.append(nn.Dropout())
      blocks.append(HijackConv2d(in_channels, 1, 1, 1, 0, bias=False))
      self.net = nn.Sequential(*blocks)
  
imports: import torch
  from torch import nn
  from torch import Tensor
  from ..misc.toolkit import download_model
  from ..misc.toolkit import set_requires_grad
  from ..modules.blocks import HijackConv2d
label_desc: Use image node_id 1 to create a new ChannelMapping object from inherited base classes: nn with example: obj = ChannelMapping(in_channels, use_dropout)