code: def parse_args():
      with open(CONFIG_JSON, "r") as f:
          valid_models = list(json.load(f).keys())
  
      parser = argparse.ArgumentParser(
          description="Script to benchmark various Merlion forecasting models on "
          "univariate forecasting task. This file assumes that "
          "you have pip installed both merlion (this repo's main "
          "package) and ts_datasets (a sub-repo)."
      )
      parser.add_argument(
          "--dataset",
          default="M4_Hourly",
          help="Name of dataset to run benchmark on. See get_dataset() "
          "in ts_datasets/ts_datasets/forecast/__init__.py for "
          "valid options.",
      )
      parser.add_argument(
          "--data_root",
          default=None,
          help="Root directory/file of dataset.",
      )
      parser.add_argument(
          "--data_kwargs",
          default="{}",
          help="JSON of keyword arguments for the data loader.",
      )
      parser.add_argument(
          "--models",
          type=str,
          nargs="*",
          default=None,
          help="Name of forecasting model to benchmark.",
          choices=valid_models,
      )
      parser.add_argument(
          "--hash",
          type=str,
          default=None,
          help="Unique identifier for the output file. Can be useful "
          "if doing multiple runs with the same model but different "
          "hyperparameters.",
      )
      parser.add_argument(
          "--ensemble_type",
          type=str,
          default="selector",
          help="How to combine multiple models in an ensemble",
          choices=["mean", "err_weighted_mean", "selector"],
      )
      parser.add_argument(
          "--retrain_type",
          type=str,
          default="without_retrain",
          help="Name of retrain type, should be one of the three "
          "types, without_retrain, sliding_window_retrain"
          "or expanding_window_retrain.",
          choices=[
              "without_retrain",
              "sliding_window_retrain",
              "expanding_window_retrain",
          ],
      )
      parser.add_argument(
          "--n_retrain",
          type=int,
          default=0,
          help="Specify the number of retrain times.",
      )
      parser.add_argument(
          "--load_checkpoint",
          action="store_true",
          default=False,
          help="Specify this option if you would like continue "
          "training your model on a dataset from a "
          "checkpoint, instead of restarting from scratch.",
      )
      parser.add_argument(
          "--debug",
          action="store_true",
          default=False,
          help="Whether to set logging level to debug.",
      )
      parser.add_argument(
          "--visualize",
          action="store_true",
          default=False,
          help="Whether to plot the model's predictions after "
          "training on each example. Mutually exclusive "
          "with running any sort of evaluation.",
      )
      parser.add_argument(
          "--summarize",
          action="store_true",
          default=False,
          help="Specify this option if you want to summarize "
          "all results for a particular dataset. Note "
          "that this option only summarizes the results "
          "that have already been computed! It does not "
          "run any algorithms, aside from the one(s) given "
          "to --models (if any).",
      )
  
      args = parser.parse_args()
      args.data_kwargs = json.loads(args.data_kwargs)
      assert isinstance(args.data_kwargs, dict)
  
      # If not summarizing all results, we need at least one model to evaluate
      if args.summarize and args.models is None:
          args.models = []
      elif not args.summarize:
          if args.models is None:
              args.models = ["ARIMA"]
          elif len(args.models) == 0:
              parser.error(
                  "At least one model required if --summarize not given"
              )
  
      return args
  
imports: import argparse
  from collections import OrderedDict
  import glob
  import json
  import logging
  import math
  import os
  import re
  import sys
  import git
  from typing import Dict, List
  import numpy
  import pandas
  import tqdm
  from merlion.evaluate.forecast import ForecastEvaluator, ForecastMetric, ForecastEvaluatorConfig
  from merlion.models.ensemble.combine import CombinerBase, Mean, ModelSelector, MetricWeightedMean
  from merlion.models.ensemble.forecast import ForecasterEnsembleConfig, ForecasterEnsemble
  from merlion.models.factory import ModelFactory
  from merlion.models.forecast.base import ForecasterBase
  from merlion.transform.resample import TemporalResample, granularity_str_to_seconds
  from merlion.utils import TimeSeries, UnivariateTimeSeries
  from merlion.utils.resample import infer_granularity, to_pd_datetime
  from ts_datasets.base import BaseDataset
  from ts_datasets.forecast import *
  import matplotlib.pyplot
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args