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: generate a graph for the forward function via scripting
docstr_tok: ["generate","a","graph","for","the","forward","function","via","scripting"]
code: def _generate_jit_forward_graph(self):
      """generate a graph for the forward function via scripting"""
      scripted_op_bench = torch.jit.script(self.op_bench)
      return scripted_op_bench.forward_consume
  
imports: import json
  import time
  import benchmark_cpp_extension
  import torch
label_desc: Use image node_id 2 for calling the PyTorchOperatorTestCase obj's underlying member method code with example usage: obj._generate_jit_forward_graph() and returns: scripted_op_bench