code: def fix_mask(self):
      cat_padding_depen = CatPaddingDependency(
          self.model, self.dummy_input, self.traced
      )
      name_to_module = {}
      for name, module in self.model.named_modules():
          name_to_module[name] = module
      depen = cat_padding_depen.dependency_sets
      for layers in depen:
          device = None
          count = 0
          for layer in layers:
              if layer in self.masks:
                  count += 1
                  if device is None:
                      device = self.masks[layer]["weight"].device
          if count == 0:
              # no layer is pruned
              continue
          elif count == len(layers):
              # all the layers have been pruned
              continue
          # pad the mask for the non-pruned layers
          for layer in layers:
              if layer in self.masks:
                  continue
  
              module = name_to_module[layer]
              w_shape = module.weight.data.size()
              w_mask = torch.ones(w_shape).to(device)
              b_mask = None
              if hasattr(module, "bias") and module.bias is not None:
                  # module.bias may be None
                  b_shape = module.bias.data.size()
                  b_mask = torch.ones(b_shape).to(device)
              self.masks[layer] = {"weight": w_mask, "bias": b_mask}
  
      return self.masks
  
imports: import os
  import logging
  import torch
  import numpy
  from .shape_dependency import ChannelDependency, GroupDependency, CatPaddingDependency, InputChannelDependency
  from .utils import get_module_by_name
label_desc: Use image node_id 2 for calling the CatMaskPadding obj's underlying member method code with example usage: obj.fix_mask() and returns: self