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: Run the backward path of an op in many iterations
docstr_tok: ["Run","the","backward","path","of","an","op","in","many","iterations"]
code: def run_backward(self, num_runs, print_per_iter=False):
      """Run the backward path of an op in many iterations"""
      # TODO: can we use JIT here to reduce python overhead?
      for _ in range(num_runs):
          self.mean.backward(retain_graph=True)
  
imports: import json
  import time
  import benchmark_cpp_extension
  import torch
label_desc: Use image node_id 7 for calling the PyTorchOperatorTestCase obj's underlying member method code with example usage: obj.run_backward(num_runs, print_per_iter) without return types