message: Update the documentation of `softmax_cross_entropy`
  This fix updates the documentation of `softmax_cross_entropy`,
  and removed the shape restrictions of `onehot_labels` and `logits`.
  They only needs to be of the same shape, not necessary `[batch_size, num_classes]`.
  This fix fixes 16263.
diff: @@ -710,11 +710,16 @@ def softmax_cross_entropy(
  new_onehot_labels = onehot_labels * (1 - label_smoothing)
  + label_smoothing / num_classes
  + Note that `onehot_labels` and `logits` must have the same shape,
  + e.g. `[batch_size, num_classes]`. The shape of `weights` must be
  + broadcastable to loss, whose shape is decided by the shape of `logits`.
  + In case the shape of `logits` is `[batch_size, num_classes]`, loss is
  + a `Tensor` of shape `[batch_size]`.
  +
  Args:
  - onehot_labels: `[batch_size, num_classes]` target one-hot-encoded labels.
  - logits: `[batch_size, num_classes]` logits outputs of the network .
  - weights: Optional `Tensor` whose rank is either 0, or rank 1 and is
  - broadcastable to the loss which is a `Tensor` of shape `[batch_size]`.
  + onehot_labels: One-hot-encoded labels.
  + logits: Logits outputs of the network.
  + weights: Optional `Tensor` that is broadcastable to loss.
  label_smoothing: If greater than 0 then smooth the labels.
  scope: the scope for the operations performed in computing the loss.
  loss_collection: collection to which the loss will be added.
  