code: def load_data():
      """Loads data."""
      if FLAGS.data_source == "tensorflow_datasets":
          return load_data_tf_datasets(
              FLAGS.dataset_name,
              FLAGS.target_num_train_per_class,
              FLAGS.target_num_val,
              FLAGS.seed,
          )
      elif FLAGS.data_source == "realistic_ssl":
          return load_data_realistic_ssl(
              FLAGS.dataset_name,
              FLAGS.filename_preprocessed_data,
              FLAGS.label_map_path,
          )
      elif FLAGS.data_source == "planetoid":
          return load_data_planetoid(
              FLAGS.dataset_name,
              FLAGS.preprocessed_data_dir,
              row_normalize=False,
          )
      raise ValueError(
          "Unsupported dataset source name: %s" % FLAGS.data_source
      )
  
file_path: neural-structured-learning/research/gam/gam/experiments/run_train_gam.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import logging
  from logging import config
  import os
  from absl import app
  from absl import flags
  from ..data.dataset import Dataset
  from ..data.loaders import load_data_planetoid
  from ..data.loaders import load_data_realistic_ssl
  from ..data.loaders import load_data_tf_datasets
  from .helper import get_model_agr
  from .helper import get_model_cls
  import numpy
  import tensorflow
  from ..trainer.trainer_cotrain import TrainerCotraining
label_desc: Use image node_id 1 for calling a global function with example usage: load_data() and returns: load_data_tf_datasets, load_data_realistic_ssl, load_data_planetoid