code: def data_process():
      train_ds, validation_ds, test_ds = tfds.load(
          "cats_vs_dogs",
          # Reserve 10% for validation and 10% for test
          split=["train[:40%]", "train[40%:50%]", "train[50%:60%]"],
          as_supervised=True,  # Include labels
      )
  
      print(
          "Number of training samples: %d"
          % tf.data.experimental.cardinality(train_ds)
      )
      print(
          "Number of validation samples: %d"
          % tf.data.experimental.cardinality(validation_ds)
      )
      print(
          "Number of test samples: %d"
          % tf.data.experimental.cardinality(test_ds)
      )
  
      return train_ds, validation_ds, test_ds
  
file_path: BigDL/python/orca/tutorial/tf/transfer_learning.py
imports: import math
  import tensorflow
  import tensorflow_datasets
  from tensorflow import keras
  from tensorflow.keras import layers
  from bigdl.orca import init_orca_context, stop_orca_context
  from bigdl.orca.learn.tf2 import Estimator
label_desc: Use image node_id 1 for calling a global function with example usage: data_process() and returns: train_ds,  validation_ds,  test_ds