message: Correct a misstatement in the documentation of the sparse softmax cross entropy logit parameter. This parameter's values do not actually have to be log probabilities.
diff: @@ -3036,7 +3036,8 @@ def softmax_cross_entropy_with_logits_v2(labels, logits, axis=-1, name=None):
  probability distribution e.g. for the case in which labels are of shape
  `[batch_size, num_classes]`, each row of `labels[i]` must be a valid
  probability distribution.
  - logits: Unscaled log probabilities.
  + logits: Per-label activations, typically a linear output. These activation
  + energies are interpreted as unnormalized log probabilities.
  axis: The class dimension. Defaulted to -1 which is the last dimension.
  name: A name for the operation (optional).
  @@ -3221,7 +3222,8 @@ def softmax_cross_entropy_with_logits(
  probability distribution e.g. for the case in which labels are of shape
  `[batch_size, num_classes]`, each row of `labels[i]` must be a valid
  probability distribution.
  - logits: Unscaled log probabilities.
  + logits: Per-label activations, typically a linear output. These activation
  + energies are interpreted as unnormalized log probabilities.
  dim: The class dimension. Defaulted to -1 which is the last dimension.
  name: A name for the operation (optional).
  axis: Alias for dim.
  @@ -3284,9 +3286,10 @@ def sparse_softmax_cross_entropy_with_logits(
  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
  + logits: Per-label activations (typically a linear output) of shape
  `[d_0, d_1, ..., d_{r-1}, num_classes]` and dtype `float16`, `float32`, or
  - `float64`.
  + `float64`. These activation energies are interpreted as unnormalized log
  + probabilities.
  name: A name for the operation (optional).
  Returns:
  