class_docstr: The multi-channel version of the 2D FCN module.
  Adds a projection layer to take arbitrary number of inputs.
  
  Args:
      in_channels: number of input channels. Defaults to 3.
      out_channels: number of output channels. Defaults to 1.
      upsample_mode: [``"transpose"``, ``"bilinear"``]
          The mode of upsampling manipulations.
          Using the second mode cannot guarantee the model's reproducibility. Defaults to ``bilinear``.
  
          - ``transpose``, uses transposed convolution layers.
          - ``bilinear``, uses bilinear interpolate.
      pretrained: If True, returns a model pre-trained on ImageNet
      progress: If True, displays a progress bar of the download to stderr.
class_docstr_tok: ["The","multi-channel","version","of","the","2D","FCN","module",".","Adds","a","projection","layer","to","take","arbitrary","number","of","inputs",".","Args",":","in_channels",":","number","of","input","channels",".","Defaults","to","3.","out_channels",":","number","of","output","channels",".","Defaults","to","1.","upsample_mode",":","[","``","``","transpose","''","``",",","``","``","bilinear","''","``","]","The","mode","of","upsampling","manipulations",".","Using","the","second","mode","can","not","guarantee","the","model","'s","reproducibility",".","Defaults","to","``","bilinear","``",".","-","``","transpose","``",",","uses","transposed","convolution","layers",".","-","``","bilinear","``",",","uses","bilinear","interpolate",".","pretrained",":","If","True",",","returns","a","model","pre-trained","on","ImageNet","progress",":","If","True",",","displays","a","progress","bar","of","the","download","to","stderr","."]
docstr: Args:
      x: in shape (batch, in_channels, spatial_1, spatial_2).
docstr_tok: ["Args",":","x",":","in","shape","(","batch",",","in_channels",",","spatial_1",",","spatial_2",")","."]
code: def forward(self, x: torch.Tensor):
      """
      Args:
          x: in shape (batch, in_channels, spatial_1, spatial_2).
      """
      x = self.init_proj(x)
      return super().forward(x)
  
imports: from __future__ import annotations
  import torch
  import torch.nn
  import torch.nn.functional
  from monai.networks.blocks.convolutions import Convolution
  from monai.networks.blocks.upsample import UpSample
  from monai.networks.layers.factories import Act, Conv, Norm
  from monai.utils import optional_import
label_desc: Use image node_id 2 for calling the MCFCN obj's underlying member method code with example usage: obj.forward(x) and returns: super