class_docstr: Represents a debugger-decorated graph.
docstr: Prune (non-control) edges related to debug ops.
  
  Prune the Copy ops and associated _Send ops inserted by the debugger out
  from the non-control inputs and output recipients map. Replace the inputs
  and recipients with original ones.
docstr_tok: ["Prune","(","non-control",")","edges","related","to","debug","ops",".","Prune","the","Copy","ops","and","associated","_Send","ops","inserted","by","the","debugger","out","from","the","non-control","inputs","and","output","recipients","map",".","Replace","the","inputs","and","recipients","with","original","ones","."]
code: def _prune_non_control_edges_of_debug_ops(self):
      """Prune (non-control) edges related to debug ops.
  
      Prune the Copy ops and associated _Send ops inserted by the debugger out
      from the non-control inputs and output recipients map. Replace the inputs
      and recipients with original ones.
      """
      for node in self._node_inputs:
          inputs = self._node_inputs[node]
  
          for i, inp in enumerate(inputs):
              if is_copy_node(inp):
                  # Find the input to the Copy node, which should be the original
                  # input to the node.
                  orig_inp = self._node_inputs[inp][0]
                  inputs[i] = orig_inp
  
imports: from tensorflow.core.framework import graph_pb2
  from tensorflow.python.framework import op_def_registry
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 5 for calling the DebugGraph obj's underlying member method code with example usage: obj._prune_non_control_edges_of_debug_ops() without return types