message: Improve description for `Reduction.NONE`.
  Improve docs for: tensorflow/tensorflow#27190
  Fixes: tensorflow/tensorflow#48743
diff: @@ -37,10 +37,21 @@ class ReductionV2(object):
  used with `tf.distribute.Strategy`, outside of built-in training loops such
  as `tf.keras` `compile` and `fit`, we expect reduction value to be
  `SUM` or `NONE`. Using `AUTO` in that case will raise an error.
  - * `NONE`: Weighted losses with one dimension reduced (axis=-1, or axis
  - specified by loss function). When this reduction type used with built-in
  - Keras training loops like `fit`/`evaluate`, the unreduced vector loss is
  - passed to the optimizer but the reported loss will be a scalar value.
  + * `NONE`: No **additional** reduction is applied to the output of the wrapped
  + loss function. When non-scalar losses are returned to Keras functions like
  + `fit`/`evaluate`, the unreduced vector loss is passed to the optimizer
  + but the reported loss will be a scalar value.
  +
  + Caution: **Verify the shape of the outputs when using** `Reduction.NONE`.
  + The builtin loss functions wrapped by the loss classes reduce
  + one dimension (`axis=-1`, or `axis` if specified by loss function).
  + `Reduction.NONE` just means that no **additional** reduction is applied by
  + the class wrapper. For categorical losses with an example input shape of
  + `[batch, W, H, n_classes]` the `n_classes` dimension is reduced. For
  + pointwise losses your must include a dummy axis so that `[batch, W, H, 1]`
  + is reduced to `[batch, W, H]`. Without the dummy axis `[batch, W, H]`
  + will be incorrectly reduced to `[batch, W]`.
  +
  * `SUM`: Scalar sum of weighted losses.
  * `SUM_OVER_BATCH_SIZE`: Scalar `SUM` divided by number of elements in losses.
  This reduction type is not supported when used with
  