message: Changed examples and comments to show difference by repeating elements
diff: @@ -527,6 +527,9 @@ class DatasetV2(tracking_base.Trackable, composite_tensor.CompositeTensor):
  def from_tensors(tensors):
  """Creates a `Dataset` with a single element, comprising the given tensors.
  + `from_tensors` produces a dataset containing only a single element. To slice
  + the input tensor into multiple elements, use `from_tensor_slices` instead.
  +
  >>> dataset = tf.data.Dataset.from_tensors([1, 2, 3])
  >>> list(dataset.as_numpy_iterator())
  [array([1, 2, 3], dtype=int32)]
  @@ -534,12 +537,12 @@ class DatasetV2(tracking_base.Trackable, composite_tensor.CompositeTensor):
  >>> list(dataset.as_numpy_iterator())
  [(array([1, 2, 3], dtype=int32), b'A')]
  - >>> # `from_tensors` does not change the shape of input tensor.
  - >>> # use `from_tensor_slices` to slice the input tensor.
  + >>> # You can use `from_tensors` to produce a dataset which repeats
  + >>> # the same example many times.
  >>> 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)
  + >>> dataset = tf.data.Dataset.from_tensors(example).repeat(2)
  + >>> list(dataset1.as_numpy_iterator())
  + [array([1, 2, 3], dtype=int32), array([1, 2, 3], dtype=int32)]
  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
  @@ -618,12 +621,13 @@ class DatasetV2(tracking_base.Trackable, composite_tensor.CompositeTensor):
  [3, 2]], dtype=int32), array([[b'A'],
  [b'B']], dtype=object))
  - >>> # `from_tensor_slices` slices the input tensor,
  - >>> # unlike `from_tensors` which retains the shape of input tensor.
  + >>> # `from_tensor_slices` can also be used to repeat the examples
  + >>> # in the data. But it doesn't repeat the whole tensor like `from_tensors`,
  + >>> # instead it repeats the individual elements (slices) from the example.
  >>> 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,)
  + >>> dataset = tf.data.Dataset.from_tensor_slices(example).repeat(2)
  + >>> list(dataset1.as_numpy_iterator())
  + [1, 2, 3, 1, 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
  