code: def testGraphConstructionInputsAndGraphAreCapturedCorrectly(self):
      instrument = _NumpyFunctionCallback(instrument_graph_ops=False)
  
      op_callbacks.add_op_callback(instrument.callback)
  
      @def_function.function
      def log_2plus_unique_x(x):
          unique_values, unique_pos = array_ops.unique(x)
          return math_ops.log(2.0 + unique_values), unique_pos
  
      x = constant_op.constant([-1.0, -1.0, 0.0], dtype=dtypes.float32)
      y1, y2 = log_2plus_unique_x(x)
      self.assertAllClose(y1, [0.0, np.log(2.0)])
      self.assertAllClose(y2, [0, 0, 1])
  
      # Check the recorded input tensors.
      self.assertEqual(
          len(instrument.graph_inputs), len(instrument.graph_op_types)
      )
      unique_inputs = instrument.graph_inputs[
          instrument.graph_op_types.index(_UNIQUE_OP)
      ]
      self.assertIsInstance(unique_inputs, tuple)
      self.assertEqual(len(unique_inputs), 1)
      self.assertEqual(
          compat.as_bytes(unique_inputs[0].op.op_def.name),
          _PLACEHOLDER_OP,
      )
  
      add_inputs = instrument.graph_inputs[
          instrument.graph_op_types.index(_ADD_OP)
      ]
      self.assertIsInstance(add_inputs, tuple)
      self.assertEqual(len(add_inputs), 2)
      self.assertEqual(
          compat.as_bytes(add_inputs[0].op.op_def.name), _CONSTANT_OP
      )
      self.assertEqual(
          compat.as_bytes(add_inputs[1].op.op_def.name), _UNIQUE_OP
      )
  
      log_inputs = instrument.graph_inputs[
          instrument.graph_op_types.index(_LOG_OP)
      ]
      self.assertIsInstance(log_inputs, tuple)
      self.assertEqual(len(log_inputs), 1)
      self.assertEqual(
          compat.as_bytes(log_inputs[0].op.op_def.name), _ADD_OP
      )
  
      # Check the recorded graphs.
      self.assertEqual(
          len(instrument.graph_graphs), len(instrument.graph_op_types)
      )
      self.assertGreater(len(instrument.graph_graph_versions), 1)
      if context.executing_eagerly():
          for i in range(len(instrument.graph_graph_versions) - 1):
              self.assertGreater(
                  instrument.graph_graph_versions[i + 1],
                  instrument.graph_graph_versions[i],
              )
  
imports: import threading
  import numpy
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import context
  from tensorflow.python.eager import def_function
  from tensorflow.python.eager import test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import op_callbacks
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import script_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.util import compat
label_desc: Use image node_id 11 for calling the OpCallbacksTest obj's underlying member method code with example usage: obj.testGraphConstructionInputsAndGraphAreCapturedCorrectly() and returns: math_ops,  unique_pos