}, {
		name:                 "readyReplicas greater than replicas",
		replicas:             3,
		fullyLabeledReplicas: 3,
		readyReplicas:        4,
		availableReplicas:    3,
		observedGeneration:   1,
		expectedErr:          true,
	}, {
		name:                 "availableReplicas greater than replicas",
		replicas:             3,
		fullyLabeledReplicas: 3,
		readyReplicas:        3,

            "type": "string"
          },
          "replicas": {
            "description": "replicas is the desired number of replicas of the given Template. These are replicas in the sense that they are instantiations of the same Template, but individual replicas also have a consistent identity. If unspecified, defaults to 1.",
            "format": "int32",
            "type": "integer"

  let summary = "An Op to exchange data across TPU replicas.";

  let description = [{
On each replica, the input is split into `split_count` blocks along
`split_dimension` and send to the other replicas given group_assignment. After
receiving `split_count` - 1 blocks from other replicas, we concatenate the
blocks along `concat_dimension` as the output.

For example, suppose there are 2 TPU replicas:
replica 0 receives input: `[[A, B]]`

        class to return a `tf.experimental.Optional` instance that contains the
        next value for all replicas or none instead of raising an out of range
        error. Also see *new*
        [guide on input distribution](https://www.tensorflow.org/tutorials/distribute/input).
*   Allow var.assign on MirroredVariables with aggregation=NONE in replica
    context. Previously this would raise an error. We now allow this because