code: def get_params():
      parser = argparse.ArgumentParser()
      # Data path
      parser.add_argument(
          "--datastore",
          type=str,
          dest="datastore",
          help="Datastore path",
      )
      parser.add_argument(
          "--train-datapath", type=str, dest="train_datapath"
      )
      parser.add_argument(
          "--validation-datapath", type=str, dest="validation_datapath"
      )
      parser.add_argument(
          "--surprise-reader", type=str, dest="surprise_reader"
      )
      parser.add_argument(
          "--usercol", type=str, dest="usercol", default="userID"
      )
      parser.add_argument(
          "--itemcol", type=str, dest="itemcol", default="itemID"
      )
      # Metrics
      parser.add_argument(
          "--rating-metrics",
          type=str,
          nargs="*",
          dest="rating_metrics",
          default=[],
      )
      parser.add_argument(
          "--ranking-metrics",
          type=str,
          nargs="*",
          dest="ranking_metrics",
          default=[],
      )
      parser.add_argument("--k", type=int, dest="k", default=None)
      parser.add_argument(
          "--remove-seen", dest="remove_seen", action="store_false"
      )
      # Training parameters
      parser.add_argument(
          "--random-state", type=int, dest="random_state", default=0
      )
      parser.add_argument(
          "--verbose", dest="verbose", action="store_true"
      )
      parser.add_argument(
          "--epochs", type=int, dest="n_epochs", default=30
      )
      parser.add_argument(
          "--biased", dest="biased", action="store_true"
      )
      parser.add_argument(
          "--primary-metric", dest="primary_metric", default="rmse"
      )
      # Hyperparameters to be tuned
      parser.add_argument(
          "--n_factors", type=int, dest="n_factors", default=100
      )
  
      return parser.parse_args()
  
file_path: recommenders/recommenders/tuning/nni/ncf_training.py
imports: import argparse
  import json
  import logging
  import numpy
  import os
  import pandas
  import nni
  import recommenders.evaluation.python_evaluation
  from recommenders.models.ncf.ncf_singlenode import NCF
  from recommenders.models.ncf.dataset import Dataset
  from recommenders.utils.constants import SEED
label_desc: Use image node_id 3 for calling a global function with example usage: get_params() and returns: parser