code: def cat_inshape(module_masks, mask, cat_info, last_visited):
      """
      Inference the output mask of the cat operation from the
      input mask.
  
      Parameters
      ----------
      module_masks : ModuleMasks
          The ModuleMasks instance of the Conv2d
      mask : CoarseMask
          The mask of its input tensor
      cat_info: dict
          Dict object that records the necessary information
          of cat operation, such as the order of the input
          tensors.
      last_visited: str
          The unique_name of the last visited node group.
  
      Returns
      -------
      CoarseMask
          The mask of its output tensor
  
      """
      assert isinstance(mask, CoarseMask)
      out_shape = cat_info["out_shape"]
      cat_dim = cat_info["cat_dim"]
      in_order = cat_info["in_order"]
      in_shape = cat_info["in_shape"]
      if module_masks.output_mask is None:
          # First visit to this cat node
          # initialize the mask based on
          # the number of the output channel.
          output_mask = CoarseMask(num_dim=len(out_shape))
          for dim, _ in enumerate(out_shape):
              if dim == cat_dim:
                  if mask.mask_index[dim] is None:
                      continue
                  device = mask.mask_index[dim].device
                  # calculate the offset of the mask
                  pos = in_order.index(last_visited)
                  offsets = [
                      in_shape[i][cat_dim]
                      for i, _ in enumerate(in_shape)
                  ]
                  offset = 0
                  for i in range(pos):
                      offset += offsets[i]
                  _tmp_mask = (mask.mask_index[dim] + offset).to(device)
                  output_mask.mask_index[dim] = _tmp_mask
              else:
                  # directly copy the mask
                  if mask.mask_index[dim] is not None:
                      output_mask.mask_index[dim] = mask.mask_index[
                          dim
                      ].data.clone()
          module_masks.set_output_mask(output_mask)
  
          return module_masks.output_mask
      # If this cat node is already visited, we need
      # validating if the mask is legel, for cat operation,
      # the mask on the 'cat_dim' dimension should be stitched
      # together. In the other dimensions, the mask should be
      # the same, else the mask is not legal.
      for dim, _ in enumerate(out_shape):
          if dim == cat_dim:
              if mask.mask_index[dim] is None:
                  continue
              pos = in_order.index(last_visited)
              offsets = [
                  in_shape[i][cat_dim] for i, _ in enumerate(in_shape)
              ]
              offset = 0
              for i in range(pos):
                  offset += offsets[i]
              device = mask.mask_index[dim].device
              new_mask = mask.mask_index[dim] + offset
              module_masks.output_mask.mask_index[
                  dim
              ] = CoarseMask.merge_index(
                  module_masks.output_mask.mask_index[dim], new_mask
              ).to(
                  device
              )
          else:
              assert module_masks.output_mask.eq_on_dim(mask, dim)
  
      return module_masks.output_mask
  
imports: import logging
  import torch
label_desc: Use image node_id 4 for calling a global function with example usage: cat_inshape(module_masks, mask, cat_info, last_visited) and returns: module_masks, module_masks