message: Make sparse_softmax_cross_entropy_with_logits consistent
diff: @@ -3240,7 +3240,7 @@ def softmax_cross_entropy_with_logits(
  labels=labels, logits=logits, axis=dim, name=name)
  -@tf_export("nn.sparse_softmax_cross_entropy_with_logits")
  +@tf_export(v1=["nn.sparse_softmax_cross_entropy_with_logits"])
  def sparse_softmax_cross_entropy_with_logits(
  _sentinel=None, # pylint: disable=invalid-name
  labels=None,
  @@ -3363,6 +3363,60 @@ def sparse_softmax_cross_entropy_with_logits(
  else:
  return cost
  +@tf_export("nn.sparse_softmax_cross_entropy_with_logits", v1=[])
  +def sparse_softmax_cross_entropy_with_logits_v2(
  + labels=None,
  + logits=None,
  + name=None):
  + """Computes sparse softmax cross entropy between `logits` and `labels`.
  +
  + Measures the probability error in discrete classification tasks in which the
  + classes are mutually exclusive (each entry is in exactly one class). For
  + 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
  + 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`
  + on `logits` internally for efficiency. Do not call this op with the
  + output of `softmax`, as it will produce incorrect results.
  +
  + A common use case is to have logits of shape
  + `[batch_size, num_classes]` and have labels of shape
  + `[batch_size]`, but higher dimensions are supported, in which
  + case the `dim`-th dimension is assumed to be of size `num_classes`.
  + `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.**
  +
  + Args:
  + labels: `Tensor` of shape `[d_0, d_1, ..., d_{r-1}]` (where `r` is rank of
  + `labels` and result) and dtype `int32` or `int64`. Each entry in `labels`
  + must be an index in `[0, num_classes)`. Other values will raise an
  + exception when this op is run on CPU, and return `NaN` for corresponding
  + loss and gradient rows on GPU.
  + logits: Unscaled log probabilities of shape
  + `[d_0, d_1, ..., d_{r-1}, num_classes]` and dtype `float16`, `float32`, or
  + `float64`.
  + name: A name for the operation (optional).
  +
  + Returns:
  + A `Tensor` of the same shape as `labels` and of the same type as `logits`
  + with the softmax cross entropy loss.
  +
  + Raises:
  + ValueError: If logits are scalars (need to have rank >= 1) or if the rank
  + of the labels is not equal to the rank of the logits minus one.
  + """
  + return sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits, name=name)
  +
  @tf_export("nn.avg_pool", v1=["nn.avg_pool_v2"])
  def avg_pool_v2(input, ksize, strides, padding, data_format=None, name=None): # pylint: disable=redefined-builtin
  