code: def main():
      # 1. GET USER INPUTS
      parser = argparse.ArgumentParser(
          description="Run all the MXNet operator benchmarks"
      )
  
      parser.add_argument(
          "--ctx",
          type=str,
          default="cpu",
          help="Global context to run all benchmarks. By default, cpu on a "
          "CPU machine, gpu(0) on a GPU machine. "
          "Valid Inputs - cpu, gpu, gpu(0), gpu(1)...",
      )
      parser.add_argument(
          "--dtype",
          type=str,
          default="float32",
          help="DType (Precision) to run benchmarks. By default, "
          "float32. Valid Inputs - float32, float64, int32, "
          "int64",
      )
      parser.add_argument(
          "-f",
          "--output-format",
          type=str,
          default="json",
          choices=["json", "md"],
          help="Benchmark result output format. By default, json. "
          "Valid Inputs - json, md",
      )
  
      parser.add_argument(
          "-o",
          "--output-file",
          type=str,
          default="./mxnet_operator_benchmarks.json",
          help="Name and path for the " "output file.",
      )
  
      parser.add_argument(
          "-p",
          "--profiler",
          type=str,
          default="native",
          help="Use built-in CPP profiler (native) or Python"
          "time module."
          "Valid Inputs - native, python",
      )
  
      parser.add_argument(
          "--int64-tensor",
          type=str,
          default="off",
          help="Run performance tests with large tensor input"
          "data (dimension >= 2**32) or standard input data."
          "Valid Inputs - on, off",
      )
  
      parser.add_argument(
          "-w",
          "--warmup",
          type=int,
          default=25,
          help="Number of times to run for warmup."
          "Valid Inputs - positive integers",
      )
  
      parser.add_argument(
          "-r",
          "--runs",
          type=int,
          default=100,
          help="Number of runs to capture benchmark results."
          "Valid Inputs - positive integers",
      )
  
      args = parser.parse_args()
      logging.info(
          f"Running MXNet operator benchmarks with the following options: {args}"
      )
      assert not os.path.isfile(
          args.output_file
      ), f"Output file {args.output_file} already exists."
  
      # 2. RUN BENCHMARKS
      ctx = _parse_mxnet_context(args.ctx)
      dtype = args.dtype
      profiler = args.profiler
      int64_tensor = args.int64_tensor
      warmup = args.warmup
      runs = args.runs
      benchmark_results = run_all_mxnet_operator_benchmarks(
          ctx=ctx,
          dtype=dtype,
          profiler=profiler,
          int64_tensor=int64_tensor,
          warmup=warmup,
          runs=runs,
      )
  
      # Sort benchmark results alphabetically by op name
      final_benchmark_results = dict()
      for key in sorted(benchmark_results.keys()):
          final_benchmark_results[key] = benchmark_results[key]
  
      # 3. PREPARE OUTPUTS
      run_time_features = get_current_runtime_features()
      save_to_file(
          final_benchmark_results,
          args.output_file,
          args.output_format,
          run_time_features,
          profiler,
      )
  
      # 4. Generate list of MXNet operators not covered in benchmarks
      ops_not_covered = get_operators_with_no_benchmark(
          final_benchmark_results.keys()
      )
      for idx, op in enumerate(ops_not_covered):
          print(f"{idx}. {op}")
  
      return 0
  
imports: import argparse
  import logging
  import os
  import sys
  import mxnet
  from benchmark.opperf.nd_operations.unary_operators import run_mx_unary_operators_benchmarks
  from benchmark.opperf.nd_operations.binary_operators import run_mx_binary_broadcast_operators_benchmarks, run_mx_binary_element_wise_operators_benchmarks, run_mx_binary_misc_operators_benchmarks
  from benchmark.opperf.nd_operations.gemm_operators import run_gemm_operators_benchmarks
  from benchmark.opperf.nd_operations.random_sampling_operators import run_mx_random_sampling_operators_benchmarks
  from benchmark.opperf.nd_operations.reduction_operators import run_mx_reduction_operators_benchmarks
  from benchmark.opperf.nd_operations.sorting_searching_operators import run_sorting_searching_operators_benchmarks
  from benchmark.opperf.nd_operations.nn_activation_operators import run_activation_operators_benchmarks
  from benchmark.opperf.nd_operations.nn_conv_operators import run_pooling_operators_benchmarks, run_convolution_operators_benchmarks, run_transpose_convolution_operators_benchmarks
  from benchmark.opperf.nd_operations.nn_basic_operators import run_nn_basic_operators_benchmarks
  from benchmark.opperf.nd_operations.nn_optimizer_operators import run_optimizer_operators_benchmarks
  from benchmark.opperf.nd_operations.indexing_routines import run_indexing_routines_benchmarks
  from benchmark.opperf.nd_operations.nn_loss_operators import run_loss_operators_benchmarks
  from benchmark.opperf.nd_operations.linalg_operators import run_linalg_operators_benchmarks
  from benchmark.opperf.nd_operations.misc_operators import run_mx_misc_operators_benchmarks
  from benchmark.opperf.nd_operations.array_manipulation_operators import run_rearrange_operators_benchmarks, run_shape_operators_benchmarks, run_expanding_operators_benchmarks, run_rounding_operators_benchmarks, run_join_split_operators_benchmarks
  from benchmark.opperf.utils.common_utils import merge_map_list, save_to_file
  from benchmark.opperf.utils.op_registry_utils import get_operators_with_no_benchmark, get_current_runtime_features
label_desc: Use image node_id 3 for calling a global function with example usage: main() and returns: int