code: def benchmarkTwoHiddenLayerMnistEagerUnified(self):
      num_iters = 100
      duration = self._computeMnistMlpGrads(
          unified_math_ops,
          unified_nn_ops,
          tape_lib,
          True,
          num_iters,
          hidden_layers=2,
          hidden_size=100,
          batch_size=1,
      )
      self.report_benchmark(
          name="TwoHiddenLayerMnistEagerUnified",
          iters=num_iters,
          wall_time=duration,
      )
  
imports: import timeit
  from absl.testing import parameterized
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework.experimental import _unified_api
  from tensorflow.python.framework.experimental import context_stack
  from tensorflow.python.framework.experimental import def_function
  from tensorflow.python.framework.experimental import math_ops
  from tensorflow.python.framework.experimental import nn_ops
  from tensorflow.python.framework.experimental import tape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_grad
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 2 for calling the UnifiedTapeBenchmark obj's underlying member method code with example usage: obj.benchmarkTwoHiddenLayerMnistEagerUnified() without return types