message: Changed example and comments
diff: @@ -534,13 +534,12 @@ class DatasetV2(tracking_base.Trackable, composite_tensor.CompositeTensor):
  >>> list(dataset.as_numpy_iterator())
  [(array([1, 2, 3], dtype=int32), b'A')]
  - >>> # `from_tensors` adds one more dimension to the shape
  - >>> # use `from_tensor_slices` to merge 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)
  + >>> # `from_tensors` does not change the shape of input tensor.
  + >>> # use `from_tensor_slices` to slice the input tensor.
  + >>> example = tf.constant([1,2,3])
  + >>> dataset = tf.data.Dataset.from_tensors(example).repeat(1000)
  + >>> np.array(list(dataset1.as_numpy_iterator())).shape
  + (1000, 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
  @@ -619,13 +618,12 @@ 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 increases dimensionality.
  - >>> 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)
  + >>> # `from_tensor_slices` slices the input tensor,
  + >>> # unlike `from_tensors` which retains the shape of input tensor.
  + >>> example = tf.constant([1,2,3])
  + >>> dataset = tf.data.Dataset.from_tensor_slices(example).repeat(1000)
  + >>> np.array(list(dataset1.as_numpy_iterator())).shape
  + (3000,)
  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
  