code: def get_datasets(args):
      train_ds = (
          Dataset(
              os.path.join(args.train_data_path, "*.parquet"),
              part_size="500MB",
          )
          if args.train_data_path
          else None
      )
      eval_ds = (
          Dataset(
              os.path.join(args.eval_data_path, "*.parquet"),
              part_size="500MB",
          )
          if args.eval_data_path
          else None
      )
      predict_ds = (
          Dataset(
              os.path.join(args.predict_data_path, "*.parquet"),
              part_size="500MB",
          )
          if args.predict_data_path
          else None
      )
  
      return train_ds, eval_ds, predict_ds
  
imports: import json
  import logging
  import os
  from datetime import datetime
  import merlin.models.tf
  import numpy
  import tensorflow
  from merlin.io.dataset import Dataset
  from merlin.models.tf.logging.callbacks import ExamplesPerSecondCallback, WandbLogger
  from merlin.models.tf.transforms.negative_sampling import InBatchNegatives
  from merlin.schema.tags import Tags
  from .args_parsing import Task, parse_arguments
  from .mtl import get_mtl_loss_weights, get_mtl_prediction_tasks
  from .ranking_models import get_model
label_desc: Use image node_id 1 for calling a global function with example usage: get_datasets(args) and returns: train_ds,  eval_ds,  predict_ds