class_docstr: GraphItem is a TensorFlow Graph wrapper.
  
  It represents the states in-between consecutive AutoDist.kernel graph transformations.
  tf.Graph is the primary property of GraphItem, whereas MetaGraph is exported/generated on demand.
  
  A GraphItem can be constructed with either a `tf.Graph` or a `GraphDef`.
docstr: An optimized version that is aware of this method is iteratively used. It optimize based on.
  
  (1) Give an updated option, if the graph has not been updated before this query, then it will not
      calculate again. A new method considering this v2 method needs to manipulate updated outside.
  (2) Give an var_queried option, which will record which variable has been doen synchronized. If all 
      the variable associated with an update op has been synchronized, this method will not consier
      the update op next time (it will reconsider if the current loop has done processed, so the
      set/reset optimize method is necessary to set the boolean flags). This optimization is
      inspired by that the for loop in this method is executed for every update_op, which is typically
      a lot, and causes the slowness. This option is safe in that if the var_queried is not set outside,
      it will not trigger the remove op.
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code: def var_op_name_to_grad_info_v2(self):
      """
      An optimized version that is aware of this method is iteratively used. It optimize based on.
  
      (1) Give an updated option, if the graph has not been updated before this query, then it will not
          calculate again. A new method considering this v2 method needs to manipulate updated outside.
      (2) Give an var_queried option, which will record which variable has been doen synchronized. If all
          the variable associated with an update op has been synchronized, this method will not consier
          the update op next time (it will reconsider if the current loop has done processed, so the
          set/reset optimize method is necessary to set the boolean flags). This optimization is
          inspired by that the for loop in this method is executed for every update_op, which is typically
          a lot, and causes the slowness. This option is safe in that if the var_queried is not set outside,
          it will not trigger the remove op.
      """
      # if the graph has not been rewritten, return old dict instead of generating a new one
      if not self.updated:
          return self.var_op_name_to_grad_dict
      expected_var_ops = {
          var.op: (grad, var)
          for grad, var in self.grad_target_pairs.items()
      }
      res = []
      # keep a list of useful update_op
      if self.first_time_loop:
          self.useful_update_op = self.all_update_ops.copy()
      for op in self.useful_update_op:
          var_op = op.inputs[op_info.UPDATE_OP_VAR_POS].op
          on_trainable_variable = var_op in expected_var_ops
          var_scope = var_op.name
          update_op_scope = parse_name_scope(op.name)
          is_initialization = update_op_scope == var_scope
          is_saving = update_op_scope.endswith("save")
          if (
              on_trainable_variable
              and not is_initialization
              and not is_saving
              and not self._is_auxiliary(op)
          ):
              if var_op.name in res:
                  raise ValueError(
                      "A variable cannot correspond to more than one update op for now."
                  )
              res.append(var_op.name)
              self.var_op_name_to_grad_dict[
                  var_op.name
              ] = expected_var_ops[var_op] + (op,)
              if self.first_time_loop:
                  self.update_op_depend_var[op].append(var_op.name)
              # analyze what var_ops the op depends on, if all removed, then can remove this op from the loop
              assert len(self.var_queried) <= 1
              if len(self.var_queried) > 0:
                  if var_op.name == self.var_queried[0]:
                      self.var_queried.remove(var_op.name)
                      self.update_op_depend_var[op].remove(var_op.name)
                      if len(self.update_op_depend_var[op]) == 0:
                          self.useful_update_op.remove(op)
      # recalculated the dict, set the indicator
      self.updated = False
      self.first_time_loop = False
      return self.var_op_name_to_grad_dict
  
imports: import contextlib
  import copy
  import functools
  from collections import defaultdict
  from typing import Union, Callable
  from google.protobuf.any_pb2 import Any
  from tensorflow.core.framework.graph_pb2 import GraphDef
  from tensorflow.core.framework.variable_pb2 import VariableDef
  from tensorflow.core.protobuf.saver_pb2 import SaverDef
  from tensorflow.python.framework import ops
  from tensorflow.python.framework.importer import import_graph_def
  from tensorflow.python.ops.resource_variable_ops import _from_proto_fn
  from tensorflow.python.ops.variables import Variable
  from autodist.const import COLOCATION_PREFIX
  from autodist.kernel.common import op_info
  from autodist.kernel.common.utils import parse_name_scope, get_op_name
  from autodist.proto import graphitem_pb2
  from autodist.utils import logging
label_desc: Use image node_id 15 for calling the GraphItem obj's underlying member method code with example usage: obj.var_op_name_to_grad_info_v2() and returns: self, self