message: Added example of from_tensors and from_tensor_slices
diff: @@ -534,6 +534,13 @@ class DatasetV2(tracking_base.Trackable, composite_tensor.CompositeTensor):
  >>> list(dataset.as_numpy_iterator())
  [(array([1, 2, 3], dtype=int32), b'A')]
  + >>> # `from_tensors` creates 3D tensor in below example
  + >>> # unlike `from_tensor_slices` which merges the input tensor.
  + >>> dataset = tf.data.Dataset.from_tensors([tf.random_uniform([2, 3]),
  + tf.random_uniform([2, 3])])
  + >>> list(dataset.as_numpy_iterator())[0].shape
  + (2, 2, 3)
  +
  Note that if `tensors` contains a NumPy array, and eager execution is not
  enabled, the values will be embedded in the graph as one or more
  `tf.constant` operations. For large datasets (> 1 GB), this can waste
  @@ -611,6 +618,13 @@ class DatasetV2(tracking_base.Trackable, composite_tensor.CompositeTensor):
  [3, 2]], dtype=int32), array([[b'A'],
  [b'B']], dtype=object))
  + >>> # `from_tensor_slices` merges the input tensor
  + >>> # unlike `from_tensors` which will create 3D tensor in below example.
  + >>> dataset = tf.data.Dataset.from_tensor_slices([tf.random.uniform([2, 3]),
  + tf.random.uniform([2, 3])])
  + >>> list(dataset.as_numpy_iterator())[0].shape
  + (2, 3)
  +
  Note that if `tensors` contains a NumPy array, and eager execution is not
  enabled, the values will be embedded in the graph as one or more
  `tf.constant` operations. For large datasets (> 1 GB), this can waste
  