code: def get_dataset():
      batch_size = 32
      num_val_samples = 10000
  
      # Return the MNIST dataset in the form of a `tf.data.Dataset`.
      (x_train, y_train), (
          x_test,
          y_test,
      ) = keras.datasets.mnist.load_data()
  
      # Preprocess the data (these are Numpy arrays)
      x_train = x_train.reshape(-1, 784).astype("float32") / 255
      x_test = x_test.reshape(-1, 784).astype("float32") / 255
      y_train = y_train.astype("float32")
      y_test = y_test.astype("float32")
  
      # Reserve num_val_samples samples for validation
      x_val = x_train[-num_val_samples:]
      y_val = y_train[-num_val_samples:]
      x_train = x_train[:-num_val_samples]
      y_train = y_train[:-num_val_samples]
      return (
          tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(
              batch_size
          ),
          tf.data.Dataset.from_tensor_slices((x_val, y_val)).batch(
              batch_size
          ),
          tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(
              batch_size
          ),
      )
  
imports: import os
  import tensorflow
  import keras
label_desc: Use image node_id 2 for calling a global function with example usage: get_dataset() and returns: tf,  tf,  tf