class_docstr: This model analyze the group dependencis between the conv
  layers in a model.
  
  Parameters
  ----------
  model : torch.nn.Module
      The model to be analyzed.
  dummy_input : 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.
class_docstr_tok: ["This","model","analyze","the","group","dependencis","between","the","conv","layers","in","a","model",".","Parameters","--","--","--","--","--","model",":","torch.nn.Module","The","model","to","be","analyzed",".","dummy_input",":","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 dependency_sets(self):
      return self.dependency
  
imports: import csv
  import logging
  import torch
  import numpy
  from .attr import get_nested_attr
label_desc: Use image node_id 7 for calling the GroupDependency obj's underlying member method code with example usage: obj.dependency_sets() and returns: self