message: Add an example to tf.nn.sparse_softmax_cross_entropy_with_logits's docstring
diff: @@ -4178,14 +4178,14 @@ def sparse_softmax_cross_entropy_with_logits_v2(labels, logits, name=None):
  example, each CIFAR-10 image is labeled with one and only one label: an image
  can be a dog or a truck, but not both.
  - **NOTE:** For this operation, the probability of a given label is considered
  + Note: For this operation, the probability of a given label is considered
  exclusive. That is, soft classes are not allowed, and the `labels` vector
  must provide a single specific index for the true class for each row of
  `logits` (each minibatch entry). For soft softmax classification with
  a probability distribution for each entry, see
  `softmax_cross_entropy_with_logits_v2`.
  - **WARNING:** This op expects unscaled logits, since it performs a `softmax`
  + Warning: This op expects unscaled logits, since it performs a `softmax`
  on `logits` internally for efficiency. Do not call this op with the
  output of `softmax`, as it will produce incorrect results.
  @@ -4196,8 +4196,16 @@ def sparse_softmax_cross_entropy_with_logits_v2(labels, logits, name=None):
  `logits` must have the dtype of `float16`, `float32`, or `float64`, and
  `labels` must have the dtype of `int32` or `int64`.
  - **Note that to avoid confusion, it is required to pass only named arguments to
  - this function.**
  + >>> logits = tf.constant([[2., -5., .5, -.1],
  + ... [0., 0., 1.9, 1.4],
  + ... [-100., 100., -100., -100.]])
  + >>> labels = tf.constant([0, 3, 1])
  + >>> tf.nn.sparse_softmax_cross_entropy_with_logits(
  + ... labels=labels, logits=logits).numpy()
  + array([0.29750752, 1.1448325 , 0. ], dtype=float32)
  +
  + To avoid confusion, passing only named arguments to this function is
  + recommended.
  Args:
  labels: `Tensor` of shape `[d_0, d_1, ..., d_{r-1}]` (where `r` is rank of
  