class_docstr: This class includes all the information needed to benchmark an operator.
  op_bench: it's a user-defined class (child of TorchBenchmarkBase)
  which includes input and operator, .etc
  test_config: a namedtuple includes test_name, input_shape, tag, run_backward.
  When run_backward is false, the run_forward method will be executed,
  When run_backward is true, run_forward_eager and _output_mean will be
  executed to generate output. Then, run_backward will be executed.
docstr: TODO (mingzhe): it is not necessary to sum up everything by myself,
  torch.autograd.backward do take a gradient tensor. By default, it
  is the same shape as your output tensor, with all 1s.
  Mathematically, it is the same as if the output is summed together.
  So we should be able to get ride of this method.
  dummy function for gradient calculation
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code: def _output_mean(self):
      """TODO (mingzhe): it is not necessary to sum up everything by myself,
      torch.autograd.backward do take a gradient tensor. By default, it
      is the same shape as your output tensor, with all 1s.
      Mathematically, it is the same as if the output is summed together.
      So we should be able to get ride of this method.
      dummy function for gradient calculation
      """
      self.mean = self.output.mean()
  
imports: import json
  import time
  import benchmark_cpp_extension
  import torch
label_desc: Use image node_id 6 for calling the PyTorchOperatorTestCase obj's underlying member method code with example usage: obj._output_mean() without return types