docstr: This model analyze the channel dependencies between the conv
  layers in a model.
  
  Parameters
  ----------
  model : torch.nn.Module
      The model to be analyzed.
  data : torch.Tensor
      The example input data to trace the network architecture.
  traced_model : torch._C.Graph
      if we alreay has the traced graph of the target model, we donnot
      need to trace the model again.
docstr_tok: ["This","model","analyze","the","channel","dependencies","between","the","conv","layers","in","a","model",".","Parameters","--","--","--","--","--","model",":","torch.nn.Module","The","model","to","be","analyzed",".","data",":","torch.Tensor","The","example","input","data","to","trace","the","network","architecture",".","traced_model",":","torch._C.Graph","if","we","alreay","has","the","traced","graph","of","the","target","model",",","we","donnot","need","to","trace","the","model","again","."]
code: def __init__(self, model=None, dummy_input=None, traced_model=None):
      """
      This model analyze the channel dependencies between the conv
      layers in a model.
  
      Parameters
      ----------
      model : torch.nn.Module
          The model to be analyzed.
      data : torch.Tensor
          The example input data to trace the network architecture.
      traced_model : torch._C.Graph
          if we alreay has the traced graph of the target model, we donnot
          need to trace the model again.
      """
      super(ChannelDependency, self).__init__(
          model, dummy_input, traced_model
      )
  
imports: import csv
  import logging
label_desc: Use image node_id 1 to create a new ChannelDependency object from inherited base classes: Dependency with example: obj = ChannelDependency(model, dummy_input, traced_model)