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.
code: def __init__(self, op_bench, test_config):
      self.test_config = test_config
      self.op_bench = op_bench
      self.place_holder_tensor = torch.ones(1)
      self.framework = "PyTorch"
      self.time_series = []
      self._jit_forward_graph = None
  
imports: import json
  import time
  import benchmark_cpp_extension
  import torch
label_desc: Use image node_id 1 to create a new PyTorchOperatorTestCase object  with example: obj = PyTorchOperatorTestCase(op_bench, test_config)