code: def fix_mask_conflict(
      masks, model=None, dummy_input=None, traced=None
  ):
      """
      MaskConflict fix the mask conflict for the channel dependencies
      and group dependency.
  
      Parameters
      ----------
      masks : dict/str
          A dict object that stores the masks or the path of the mask file
      model : torch.nn.Module
          model to fix the mask conflict
      dummy_input : torch.Tensor
          input example to trace the model
      traced : torch._C.torch.jit.TopLevelTracedModule
          the traced model of the target model, is this parameter is not None,
          we donnot use the model and dummpy_input to get the trace graph.
      """
      if isinstance(masks, str):
          # if the input is the path of the mask_file
          assert os.path.exists(masks)
          masks = torch.load(masks)
      assert len(masks) > 0, "Mask tensor cannot be empty"
      # if the user uses the model and dummy_input to trace the model, we
      # should get the traced model handly, so that, we only trace the
      # model once, GroupMaskConflict and ChannelMaskConflict will reuse
      # this traced model.
      if traced is None:
          assert model is not None and dummy_input is not None
          training = model.training
          model.eval()
          # We need to trace the model in eval mode
          traced = torch.jit.trace(model, dummy_input)
          model.train(training)
  
      fix_group_mask = GroupMaskConflict(
          masks, model, dummy_input, traced
      )
      masks = fix_group_mask.fix_mask()
      fix_channel_mask = ChannelMaskConflict(
          masks, model, dummy_input, traced
      )
      masks = fix_channel_mask.fix_mask()
      padding_cat_mask = CatMaskPadding(
          masks, model, dummy_input, traced
      )
      masks = padding_cat_mask.fix_mask()
      return masks, fix_channel_mask.conv_prune_dim
  
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 1 for calling a global function with example usage: fix_mask_conflict(masks, model, dummy_input, traced) and returns: masks,  fix_channel_mask