"description": "Partition indicates the ordinal at which the StatefulSet should be partitioned for updates. During a rolling update, all pods from ordinal Replicas-1 to Partition are updated. All pods from ordinal Partition-1 to 0 remain untouched. This is helpful in being able to do a canary based deployment. The default value is 0.",
            "format": "int32",
            "type": "integer"
          }
        },
        "type": "object"

Arguments that are captured, are not batched. The session.run call which does
the concatenation, will use the values of the captured tensors available to it.
Therefore, typical uses of captured tensors should involve values which remain
unchanged across session.run calls. Inference is a good example of this.

SparseTensor is not supported. The return value of the decorated function
must be a Tensor or a list/tuple of Tensors.
  }];

`tf.divide()` is now the recommended division function. `tf.div()` will remain,
but its semantics do not respond to Python 3 or `from future` mechanisms. *
tf.reverse() now takes indices of axes to be reversed. E.g. `tf.reverse(a,
[True, False, True])` must now be written as `tf.reverse(a, [0, 2])`.
`tf.reverse_v2()` will remain until 1.0 final. * `tf.mul`, `tf.sub` and `tf.neg`