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 forward path of an op with JIT mode
docstr_tok: ["Run","the","forward","path","of","an","op","with","JIT","mode"]
code: def run_jit_forward(
      self, num_runs, print_per_iter=False, cuda_sync=False
  ):
      """Run the forward path of an op with JIT mode"""
      if self._jit_forward_graph is None:
          self._jit_forward_graph = self._generate_jit_forward_graph()
      self._jit_forward_graph(num_runs)
  
imports: import json
  import time
  import benchmark_cpp_extension
  import torch
label_desc: Use image node_id 3 for calling the PyTorchOperatorTestCase obj's underlying member method code with example usage: obj.run_jit_forward(num_runs, print_per_iter, cuda_sync) without return types