message: Slightly improve softmax_cross_entropy_with_logits example.
  The logits no longer add to 1, as that is not required. Also, in the second batch, the labels are not exclusive.
diff: @@ -3122,11 +3122,11 @@ def softmax_cross_entropy_with_logits_v2(labels, logits, axis=-1, name=None):
  one class is true at a time), see `sparse_softmax_cross_entropy_with_logits`.
  Usage:
  - >>> logits = [[0.6, 0.2, 0.2], [0.0, 0.9, 0.1]]
  - >>> labels = [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]
  - >>> tf.nn.softmax_cross_entropy_with_logits(labels, logits)
  + >>> logits = [[4.0, 2.0, 1.0], [0.0, 5.0, 1.0]]
  + >>> labels = [[1.0, 0.0, 0.0], [0.0, 0.8, 0.2]]
  + >>> tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits)
  <tf.Tensor: shape=(2,), dtype=float32,
  - numpy=array([0.8504244, 0.6183691], dtype=float32)>
  + numpy=array([0.16984604, 0.82474494], dtype=float32)>
  **WARNING:** This op expects unscaled logits, since it performs a `softmax`
  on `logits` internally for efficiency. Do not call this op with the
  