message: DOC: y could be a dict
diff: @@ -52,8 +52,9 @@ def numpy_input_fn(x,
  num_threads=1):
  """Returns input function that would feed dict of numpy arrays into the model.
  - This returns a function outputting `features` and `target` based on the dict
  - of numpy arrays. The dict `features` has the same keys as the `x`.
  + This returns a function outputting `features` and `targets` based on the dict
  + of numpy arrays. The dict `features` has the same keys as the `x`. The dict
  + `targets` has the same keys as the `y` if `y` is a dict.
  Example:
  @@ -70,7 +71,7 @@ def numpy_input_fn(x,
  Args:
  x: dict of numpy array object.
  - y: numpy array object. `None` if absent.
  + y: numpy array object or dict of numpy array object. `None` if absent.
  batch_size: Integer, size of batches to return.
  num_epochs: Integer, number of epochs to iterate over data. If `None` will
  run forever.
  @@ -82,7 +83,7 @@ def numpy_input_fn(x,
  such as in prediction and evaluation mode, `num_threads` should be 1.
  Returns:
  - Function, that has signature of ()->(dict of `features`, `target`)
  + Function, that has signature of ()->(dict of `features`, `targets`)
  Raises:
  ValueError: if the shape of `y` mismatches the shape of values in `x` (i.e.,
  @@ -130,7 +131,7 @@ def numpy_input_fn(x,
  raise ValueError('Length of tensors in x and y is mismatched. All '
  'elements in x and y must have the same length.\n'
  'Shapes in x: {}\n'
  - 'Shape for y: {}\n'.format(shape_dict_of_x, shape_of_y))
  + 'Shape in y: {}\n'.format(shape_dict_of_x, shape_of_y))
  queue = feeding_functions._enqueue_data( # pylint: disable=protected-access
  ordered_dict_data,
  