code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Benchmark sklearn/HB on iris"
      )
      parser.add_argument(
          "-dataset",
          default="iris",
          type=str,
          help="The dataset to be used for benchmarking.",
      )
      parser.add_argument(
          "-datadir",
          default=os.path.join(
              ROOT_PATH, "benchmarks/operators/datasets/"
          ),
          type=str,
          help="The root datasets folder",
      )
      parser.add_argument(
          "-modeldir",
          default=os.path.join(
              ROOT_PATH, "benchmarks/operators/models/"
          ),
          type=str,
          help="The root models folder",
      )
      parser.add_argument(
          "-operator",
          default="all",
          type=str,
          help=(
              "Comma-separated list of operators to run; "
              "'all' run all"
          ),
      )
      parser.add_argument(
          "-backend",
          default="all",
          type=str,
          help=(
              "Comma-separated list of train algorithms to run; "
              "'all' run onnx-ml, hb-torchscript, hb-tvm"
          ),
      )
      parser.add_argument(
          "-cpus",
          default=1,
          type=int,
          help=(
              "#CPUs to use for the benchmarks; "
              "0 means psutil.cpu_count(logical=False)"
          ),
      )
      parser.add_argument(
          "-batch_size",
          default=1000000,
          type=int,
          help=(
              "Supported batch size. By default we score one record at a time."
          ),
      )
      parser.add_argument(
          "-gpu",
          default=False,
          action="store_true",
          help=(
              "Whether to run scoring on SPU or not.  Adding this flag uses gpu"
          ),
      )
      parser.add_argument(
          "-output",
          default=None,
          type=str,
          help="Output json file with runtime/accuracy stats",
      )
      parser.add_argument(
          "-nrows",
          default=1000000,
          type=int,
          help=(
              "Subset of rows in the datasets to use. Useful for test running "
              "benchmarks on small amounts of data. WARNING: Some datasets will "
              "give incorrect accuracy results if nrows is specified as they have "
              "predefined train/test splits."
          ),
      )
      parser.add_argument(
          "-niters",
          default=5,
          type=int,
          help=("Number of iterations for each experiment"),
      )
      parser.add_argument(
          "-validate",
          default=False,
          help="Validate prediction output and fails accordigly.",
      )
      parser.add_argument(
          "-extra",
          default="{}",
          help="Extra arguments as a python dictionary",
      )
      args = parser.parse_args()
      # default value for output json file
      if not args.output:
          args.output = "result-{}-{}.json".format(
              "gpu" if args.gpu else "cpu", args.nrows
          )
      return args
  
imports: import os
  import sys
  import argparse
  import json
  import ast
  from pathlib import Path
  import psutil
  import signal
  import numpy
  import warnings
  import gc
  from scipy import stats
  from memory_profiler import memory_usage
  from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
  from sklearn.linear_model.stochastic_gradient import SGDClassifier
  from sklearn.naive_bayes import BernoulliNB
  from sklearn.neural_network import MLPClassifier
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.preprocessing.data import Binarizer, MaxAbsScaler, MinMaxScaler, Normalizer, PolynomialFeatures, RobustScaler, StandardScaler
  from sklearn.svm.classes import LinearSVC, NuSVC, SVC
  import benchmarks.operators.train
  import benchmarks.operators.score
  from benchmarks.datasets import prepare_dataset, LearningTask
  from hummingbird.ml._utils import sklearn_installed, onnx_ml_tools_installed, onnx_runtime_installed, tvm_installed
label_desc: Use image node_id 6 for calling a global function with example usage: parse_args() and returns: args