code: def mnist_synthetic_dataset(batch_size, steps_per_epoch):
      """Generate synthetic MNIST dataset for testing."""
      # train dataset
      x_train = tf.ones(
          [batch_size * steps_per_epoch, 28, 28, 1],
          dtype=tf.dtypes.float32,
      )
      y_train = tf.ones(
          [batch_size * steps_per_epoch, 1], dtype=tf.dtypes.int32
      )
      train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train))
      train_ds = train_ds.repeat()
      # train_ds = train_ds.shuffle(100)
      train_ds = train_ds.batch(64, drop_remainder=True)
  
      # eval dataset
      x_test = tf.random.uniform(
          [10000, 28, 28, 1], dtype=tf.dtypes.float32
      )
      y_test = tf.random.uniform(
          [10000, 1], minval=0, maxval=9, dtype=tf.dtypes.int32
      )
      eval_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test))
      eval_ds = eval_ds.batch(64, drop_remainder=True)
  
      return train_ds, eval_ds
  
label_desc: Use image node_id 1 for calling a global function with example usage: mnist_synthetic_dataset(batch_size, steps_per_epoch) and returns: train_ds,  eval_ds