docstr: ChannelMaskConflict fix the mask conflict between the layers that
  has channel dependecy with each other.
  
  Parameters
  ----------
  masks : dict
      a dict object that stores the masks
  model : torch.nn.Module
      model to fix the mask conflict
  dummy_input : torch.Tensor
      input example to trace the model
  graph : torch._C.torch.jit.TopLevelTracedModule
      the traced graph of the target model, is this parameter is not None,
      we donnot use the model and dummpy_input to get the trace graph.
docstr_tok: ["ChannelMaskConflict","fix","the","mask","conflict","between","the","layers","that","has","channel","dependecy","with","each","other",".","Parameters","--","--","--","--","--","masks",":","dict","a","dict","object","that","stores","the","masks","model",":","torch.nn.Module","model","to","fix","the","mask","conflict","dummy_input",":","torch.Tensor","input","example","to","trace","the","model","graph",":","torch._C.torch.jit.TopLevelTracedModule","the","traced","graph","of","the","target","model",",","is","this","parameter","is","not","None",",","we","donnot","use","the","model","and","dummpy_input","to","get","the","trace","graph","."]
code: def __init__(self, masks, model=None, dummy_input=None, traced=None):
      """
      ChannelMaskConflict fix the mask conflict between the layers that
      has channel dependecy with each other.
  
      Parameters
      ----------
      masks : dict
          a dict object that stores the masks
      model : torch.nn.Module
          model to fix the mask conflict
      dummy_input : torch.Tensor
          input example to trace the model
      graph : torch._C.torch.jit.TopLevelTracedModule
          the traced graph of the target model, is this parameter is not None,
          we donnot use the model and dummpy_input to get the trace graph.
      """
      super(ChannelMaskConflict, self).__init__(
          masks, model, dummy_input, traced
      )
      self.conv_prune_dim = detect_mask_prune_dim(masks, model)
      _logger.info("detected conv prune dim: %s", self.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 to create a new ChannelMaskConflict object from inherited base classes: MaskFix with example: obj = ChannelMaskConflict(masks, model, dummy_input, traced)