code: def convtranspose2d_outshape(module_masks, mask):
      assert isinstance(mask, CoarseMask)
      assert mask.mask_index[1] is not None
      assert mask.mask_index[0] is None
      assert mask.mask_index[2] is None
      assert mask.mask_index[3] is None
  
      if module_masks.output_mask is None:
          module_masks.output_mask = mask
      else:
          # mask conflict should be solved by fix_mask_conflict before speedup
          # mask and module_masks.output_mask may have different number of dimensions
          # since they could be passed by linear or conv2d
          assert all(
              module_masks.output_mask.mask_index[1]
              == mask.mask_index[1]
          )
  
      weight_cmask = CoarseMask(num_dim=4)
      # Note the memory layout of Convtranspose2d is C_in, C_out, k1, k2
      weight_cmask.add_index_mask(dim=1, index=mask.mask_index[1])
      bias_cmask = CoarseMask(num_dim=1)
      bias_cmask.add_index_mask(dim=0, index=mask.mask_index[1])
      module_masks.set_param_masks("weight", weight_cmask)
      module_masks.set_param_masks("bias", bias_cmask)
  
      # shape changes pass through depths wise conv layers
      m = module_masks.module
      if m.in_channels == m.out_channels == m.groups:
          module_masks.output_mask = mask
          module_masks.input_mask = mask
          return mask
      return None
  
imports: import logging
  import torch
label_desc: Use image node_id 26 for calling a global function with example usage: convtranspose2d_outshape(module_masks, mask) and returns: None, mask