code: def mnist_dataset(batch_size):
      (x_train, y_train), _ = tf.keras.datasets.mnist.load_data()
      # The `x` arrays are in uint8 and have values in the [0, 255] range.
      # You need to convert them to float32 with values in the [0, 1] range.
      x_train = x_train / np.float32(255)
      y_train = y_train.astype(np.int64)
      train_dataset = (
          tf.data.Dataset.from_tensor_slices((x_train, y_train))
          .shuffle(60000)
          .repeat()
          .batch(batch_size)
      )
      return train_dataset
  
imports: import json
  import os
  import tensorflow
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: mnist_dataset(batch_size) and returns: train_dataset