docstr: Get the list of the dependency set.
  
  Returns
  -------
  dependency_sets : list
      list of the dependency sets.
      Each dependency set is a 4-element list of module names, with the first three elements being the projection
      matrices for Q, K, V (in any order), and the last element being the dense matrix.
docstr_tok: ["Get","the","list","of","the","dependency","set",".","Returns","--","--","--","-","dependency_sets",":","list","list","of","the","dependency","sets",".","Each","dependency","set","is","a","4-element","list","of","module","names",",","with","the","first","three","elements","being","the","projection","matrices","for","Q",",","K",",","V","(","in","any","order",")",",","and","the","last","element","being","the","dense","matrix","."]
code: def dependency_sets(self):
      """
      Get the list of the dependency set.
  
      Returns
      -------
      dependency_sets : list
          list of the dependency sets.
          Each dependency set is a 4-element list of module names, with the first three elements being the projection
          matrices for Q, K, V (in any order), and the last element being the dense matrix.
      """
      d_sets = []
      for node in self.graph.nodes_py.nodes_op:
          if (
              node.op_type != "aten::matmul"
              or node.name not in self.dependency
              or len(self.dependency[node.name]) != 4
          ):
              continue
          d_sets.append(self.dependency[node.name])
  
      return d_sets
  
imports: import csv
  import logging
  import torch
  import numpy
  from .attr import get_nested_attr
label_desc: Use image node_id 5 for calling the AttentionWeightDependency obj's underlying member method code with example usage: obj.dependency_sets() and returns: d_sets