message: MeanIoU support y_pred passed as a logits tensor
  Just like categorical_accuracy and sparse_categorical_accuracy, this change allow MeanIoU metric support y_pred to be passed as a logits tensor.
diff: @@ -2802,6 +2802,7 @@ class MeanIoU(Metric):
  This value must be provided, since a confusion matrix of dimension =
  [num_classes, num_classes] will be allocated.
  name: (Optional) string name of the metric instance.
  + from_logits: (Optional) Whether `y_pred` is expected to be a logits tensor.
  dtype: (Optional) data type of the metric result.
  Standalone usage:
  @@ -2832,9 +2833,10 @@ class MeanIoU(Metric):
  ```
  """
  - def __init__(self, num_classes, name=None, dtype=None):
  + def __init__(self, num_classes, name=None, from_logits=False, dtype=None):
  super(MeanIoU, self).__init__(name=name, dtype=dtype)
  self.num_classes = num_classes
  + self.from_logits = from_logits
  # Variable to accumulate the predictions in the confusion matrix.
  self.total_cm = self.add_weight(
  @@ -2855,6 +2857,8 @@ class MeanIoU(Metric):
  Returns:
  Update op.
  """
  + if self.from_logits:
  + y_pred = math_ops.argmax(y_pred, axis=-1)
  y_true = math_ops.cast(y_true, self._dtype)
  y_pred = math_ops.cast(y_pred, self._dtype)
  