code: def run_benchmark(
      workload,
      model_name,
      out_file_name,
      is_train,
      use_tvm,
      instance_type,
      use_fp16,
  ):
      if is_train:
          benchmark = GluonNLPBackboneBenchmark(
              workloads=workload,
              model_names=model_name,
              profile_inference=False,
              profile_train=True,
              to_csv=True,
              use_fp16=use_fp16,
              train_out_csv_file=out_file_name,
          )
          benchmark.run()
      else:
          benchmark = GluonNLPBackboneBenchmark(
              workloads=workload,
              model_names=model_name,
              profile_inference=True,
              profile_train=False,
              use_tvm=use_tvm,
              instance_type=instance_type,
              to_csv=True,
              use_fp16=use_fp16,
              inference_out_csv_file=out_file_name,
          )
          benchmark.run()
      return
  
file_path: gluon-nlp/docs/model_zoo/benchmarks/benchmark_gluonnlp.py
imports: import mxnet
  import argparse
  import os
  import pandas
  from benchmark_utils import GluonNLPBackboneBenchmark
  import multiprocessing
  from multiprocessing import Process
label_desc: Use image node_id 2 for calling a global function with example usage: run_benchmark(workload, model_name, out_file_name, is_train, use_tvm, instance_type, use_fp16) without return types