code: def build_arg_parser():
      parser = argparse.ArgumentParser(
          description="Preprocessing script for ranking models using NVTabular and dask-cudf on GPUs"
      )
  
      # Inputs / Outputs
      parser.add_argument(
          "--data_path", default="", help="Path to the data"
      )
      parser.add_argument(
          "--eval_data_path",
          default=None,
          help="Path to eval data, if data was already split"
          "Must have the same schema as train data (in --data_path).",
      )
      parser.add_argument(
          "--predict_data_path",
          default=None,
          help="Path to data to be preprocessed for prediction."
          "This data is expected to have the same input features as train data but not targets, "
          "as this data is used for prediction.",
      )
      parser.add_argument(
          "--input_data_format",
          default="csv",
          choices=["csv", "tsv", "parquet"],
          help="Input data format",
      )
  
      parser.add_argument(
          "--csv_sep",
          default=",",
          help="Character separator for CSV files."
          "Default is ','. You can use 'tab' for tabular separated data, or "
          "--input_data_format tsv",
      )
      parser.add_argument(
          "--csv_na_values",
          default=None,
          help="String in the original data that should be "
          "replaced by NULL",
      )
  
      parser.add_argument(
          "--output_path",
          default="./results/",
          help="Output path where the preprocessed files "
          "and nvtabular workflow will be saved"
          "Default is ./results/",
      )
      parser.add_argument(
          "--output_num_partitions",
          default=10,
          type=int,
          help="Number of partitions "
          "that result in this number of output files"
          "Default is 10.",
      )
      parser.add_argument(
          "--persist_intermediate_files",
          type=str2bool,
          nargs="?",
          const=True,
          default=False,
          help="Whether to persist/cache the intermediate preprocessing files. "
          "Enabling this might be necessary for larger datasets.",
      )
  
      parser.add_argument(
          "--control_features",
          default="",
          help="Columns (comma-separated) that should be kept as is in the output files. "
          "For example, --control_features=session_id,timestamp",
      )
      parser.add_argument(
          "--categorical_features",
          default="",
          help="Columns (comma-sep) with categorical/discrete features "
          "that will encoded/categorified to contiguous ids in the preprocessing. "
          "These tags are tagged as 'categorical' in the schema, "
          "so that Merlin Models can automatically create "
          "embedding tables for them.",
      )
      parser.add_argument(
          "--continuous_features",
          default="",
          help="Columns (comma-sep) with continuous features that will be standardized "
          "and tagged in the schema as 'continuous', so that the Merlin Models can "
          "represent and combine them with embedding properly.",
      )
  
      parser.add_argument(
          "--continuous_features_fillna",
          default=None,
          help="Replaces NULL values with this float. "
          "You can also set it with 'median' for filling nulls with the median value.",
      )
  
      parser.add_argument(
          "--user_features",
          default="",
          help="Columns (comma-sep) that should be tagged in the schema  as user features. "
          "This information might be useful for modeling later.",
      )
      parser.add_argument(
          "--item_features",
          default="",
          help="Columns (comma-sep) that should be tagged in the schema as item features. "
          "This information might be useful for modeling later, "
          "for example, for in-batch sampling if your data contains only positive examples.",
      )
      parser.add_argument(
          "--binary_classif_targets",
          default="",
          help="Columns (comma-sep) that should be tagged in the schema as binary target. "
          "Merlin Models will create a binary classification head for each of these targets.",
      )
      parser.add_argument(
          "--regression_targets",
          default="",
          help="Columns (comma-sep) that should be tagged in the schema as binary target. "
          "Merlin Models will create a regression head for each of these targets.",
      )
      parser.add_argument(
          "--target_encoding_features",
          default="",
          help="Columns (comma-sep) with categorical/discrete features "
          "for which target encoding features will be generated, with "
          "the average of the target columns for each categorical value. "
          "The target columns are defined in --target_encoding_targets. "
          "If --target_encoding_features is not provided but --target_encoding_targets "
          "is, all categorical features will be used.",
      )
      parser.add_argument(
          "--target_encoding_targets",
          default="",
          help="Columns (comma-sep) with target columns "
          "that will be used to compute target encoding features "
          "with the average of the target columns for categorical features value. "
          "The categorical features are defined in --target_encoding_features. "
          "If --target_encoding_targets is not provided but --target_encoding_features is, "
          "all target columns will be used.",
      )
  
      parser.add_argument(
          "--target_encoding_kfold",
          default=5,
          type=int,
          help="Number of folds for target encoding, in order to avoid that the current example "
          "is considered in the target encoding feature computation, which could cause "
          "overfitting for infrequent categorical values. Default is 5",
      )
  
      parser.add_argument(
          "--target_encoding_smoothing",
          default=10,
          type=int,
          help="Smoothing factor that is used in the target encoding computation, as statistics for "
          "infrequent categorical values might be noisy. "
          "It makes target encoding formula = "
          "`sum_target_per_categ_value + (global_target_avg * smooth) / categ_value_count + smooth`. "
          "Default is 10",
      )
  
      parser.add_argument(
          "--user_id_feature",
          default="",
          help="Column that contains the user id feature, for tagging in the schema. "
          "This information is used in the preprocessing "
          "if you set --min_user_freq or --max_user_freq",
      )
      parser.add_argument(
          "--item_id_feature",
          default="",
          help="Column that contains the item id feature, for tagging in the schema. "
          "This information is used in the preprocessing "
          "if you set --min_item_freq or --max_item_freq",
      )
      parser.add_argument(
          "--timestamp_feature",
          default="",
          help="Column containing a timestamp or date feature. "
          "The basic preprocessing doesn't extracts date and time features for it. "
          "It is just tagged as 'timestamp' in "
          "the schema and used for splitting train / eval data "
          "if --dataset_split_strategy=temporal is used.",
      )
      parser.add_argument(
          "--session_id_feature",
          default="",
          help="This is just for tagging this feature.",
      )
  
      # parser.add_argument("--groupby_feature", default="", help="")
  
      parser.add_argument(
          "--to_int32",
          default="",
          help="Cast these columns (comma-sep) to int32.",
      )
      parser.add_argument(
          "--to_int16",
          default="",
          help="Cast these columns (comma-sep) to int16, to save some memory.",
      )
      parser.add_argument(
          "--to_int8",
          default="",
          help="Cast these columns (comma-sep) to int32, to save some memory.",
      )
      parser.add_argument(
          "--to_float32",
          default="",
          help="Cast these columns (comma-sep) to float32",
      )
  
      parser.add_argument(
          "--categ_min_freq_capping",
          default=0,
          type=int,
          help="Value used for min frequency capping. If greater than 0, "
          "all categorical values which are less frequent than this "
          "threshold will be mapped to the null value encoded id.",
      )
  
      parser.add_argument(
          "--min_user_freq",
          default=None,
          type=int,
          help="Users with frequency lower than this value "
          "are removed from the dataset (before data splitting).",
      )
      parser.add_argument(
          "--max_user_freq",
          default=None,
          type=int,
          help="Users with frequency higher than this value "
          "are removed from the dataset (before data splitting).",
      )
      parser.add_argument(
          "--min_item_freq",
          default=None,
          type=int,
          help="Items with frequency lower than this value "
          "are removed from the dataset (before data splitting).",
      )
      parser.add_argument(
          "--max_item_freq",
          default=None,
          type=int,
          help="Items with frequency higher than this value "
          "are removed from the dataset (before data splitting).",
      )
      parser.add_argument(
          "--num_max_rounds_filtering",
          default=5,
          type=int,
          help="Max number of rounds interleaving users and items frequency filtering. "
          "If a small number of rounds is chosen, some low-frequent users or items "
          "might be kept in the dataset. Default is 5",
      )
  
      parser.add_argument(
          "--filter_query",
          default=None,
          type=str,
          help="A filter query condition compatible with dask-cudf `DataFrame.query()`",
      )
  
      parser.add_argument(
          "--dataset_split_strategy",
          default=None,
          type=str,
          choices=["random", "random_by_user", "temporal"],
          help="If None, no data split is performed. "
          "If 'random', samples are assigned randomly to eval set according to "
          "--random_split_eval_perc. "
          "If 'random_by_user', users will have examples in both train and eval set, "
          "according to the proportion "
          "specified in --random_split_eval_perc. "
          "If 'temporal', the --timestamp_feature with --dataset_split_temporal_timestamp "
          "to split eval set based on time.",
      )
  
      parser.add_argument(
          "--random_split_eval_perc",
          default=None,
          type=float,
          help="Percentage of examples to be assigned to eval set. "
          "It is used with --dataset_split_strategy 'random' and 'random_by_user'",
      )
      parser.add_argument(
          "--dataset_split_temporal_timestamp",
          default=None,
          type=int,
          help="Used when --dataset_split_strategy 'temporal'. "
          "It assigns for eval set all examples where the --timestamp_feature >= value",
      )
  
      parser.add_argument(
          "--enable_dask_cuda_cluster",
          type=str2bool,
          nargs="?",
          const=True,
          default=False,
          help="Initializes a LocalCUDACluster for multi-GPU preprocessing.",
      )
  
      parser.add_argument(
          "--dask_cuda_visible_gpu_devices",
          default=None,
          type=str,
          help="Ids of GPU devices that should be used "
          "for preprocessing, if any. For example: --visible_gpu_devices=0,1. "
          "Default is None, for using all GPUs",
      )
      parser.add_argument(
          "--dask_cuda_gpu_device_spill_frac",
          default=0.7,
          type=float,
          help="Percentage of GPU memory used at which "
          "LocalCUDACluster should spill memory to CPU, before raising "
          "out-of-memory errors. Default is 0.7",
      )
  
      return parser
  
imports: import argparse
label_desc: Use image node_id 1 for calling a global function with example usage: build_arg_parser() and returns: parser