class_docstr: ### Crop and Concatenate the feature map
  
  At every step in the expansive path the corresponding feature map from the contracting path
  concatenated with the current feature map.
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docstr: :param x: current feature map in the expansive path
  :param contracting_x: corresponding feature map from the contracting path
docstr_tok: [":","param","x",":","current","feature","map","in","the","expansive","path",":","param","contracting_x",":","corresponding","feature","map","from","the","contracting","path"]
code: def forward(self, x: torch.Tensor, contracting_x: torch.Tensor):
      """
      :param x: current feature map in the expansive path
      :param contracting_x: corresponding feature map from the contracting path
      """
  
      # Crop the feature map from the contracting path to the size of the current feature map
      contracting_x = torchvision.transforms.functional.center_crop(
          contracting_x, [x.shape[2], x.shape[3]]
      )
      # Concatenate the feature maps
      x = torch.cat([x, contracting_x], dim=1)
      #
      return x
  
imports: import torch
  import torchvision.transforms.functional
  from torch import nn
label_desc: Use image node_id 1 for calling the CropAndConcat obj's underlying member method code with example usage: obj.forward(x, contracting_x) and returns: x