class_docstr: This is a base class used to create Pytorch operator benchmark.
  module_name is the name of the operator being benchmarked.
  test_name is the name (it's created by concatenating all the
  inputs) of a specific test
docstr: this is a globally unique name which can be used to
  label a specific test
docstr_tok: ["this","is","a","globally","unique","name","which","can","be","used","to","label","a","specific","test"]
code: def test_name(self, **kargs):
      """this is a globally unique name which can be used to
      label a specific test
      """
  
      # This is a list of attributes which will not be included
      # in the test name.
      skip_key_list = ["device"]
  
      test_name_str = []
      for key in kargs:
          value = kargs[key]
          test_name_str.append(
              ("" if key in skip_key_list else key)
              + str(value if type(value) != bool else int(value))
          )
      name = (
          self.module_name() + "_" + "_".join(test_name_str)
      ).replace(" ", "")
      return name
  
imports: import json
  import time
  import benchmark_cpp_extension
  import torch
label_desc: Use image node_id 10 for calling the TorchBenchmarkBase obj's underlying member method code with example usage: obj.test_name() and returns: name