message: Improve docs for from_tensor_slices
diff: @@ -427,6 +427,29 @@ class DatasetV2(tracking_base.Trackable, composite_tensor.CompositeTensor):
  def from_tensor_slices(tensors):
  """Creates a `Dataset` whose elements are slices of the given tensors.
  + The given tensors are sliced along their first dimension. This operation
  + preserves the structure of the input tensors, removing the first dimension
  + of each tensor and using it as the dataset dimension. All input tensors
  + must have the same size in their first dimensions.
  +
  + ```python
  + # Slicing a 1D tensor produces scalar tensor elements.
  + Dataset.from_tensor_slices([1, 2, 3]) # ==> [ 1, 2, 3 ]
  +
  + # Slicing a 2D tensor produces 1D tensor elements.
  + Dataset.from_tensor_slices([[1, 2], [3, 4], [5, 6]])
  + # ==> [ [1, 2], [3, 4], [5, 6] ]
  +
  + # Slicing a tuple of 1D tensors produces tuple elements containing scalar
  + tensors.
  + Dataset.from_tensor_slices(([1, 2], [3, 4], [5, 6]))
  + # ==> [ (1, 3, 5), (2, 4, 6) ]
  +
  + # Dictionary structure is also preserved.
  + Dataset.from_tensor_slices({"a": [1, 2], "b": [3, 4], "c": [5, 6]})
  + # ==> [ {"a": 1, "b": 3, "c": 5}, {"a": 2, "b": 4, "c:" 6} ]
  + ```
  +
  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
  @@ -437,7 +460,7 @@ class DatasetV2(tracking_base.Trackable, composite_tensor.CompositeTensor):
  Args:
  tensors: A dataset element, with each component having the same size in
  - the 0th dimension.
  + the first dimension.
  Returns:
  Dataset: A `Dataset`.
  