format: int32
                                  type: integer
                                http2MaxRequests:
                                  description: Maximum number of active requests to
                                    a destination.
                                  format: int32
                                  type: integer

                                  format: int32
                                  type: integer
                                http2MaxRequests:
                                  description: Maximum number of active requests to
                                    a destination.
                                  format: int32
                                  type: integer

	// result: (MOVDconst [int64(uint32(int32(c)/int32(d)))])
	for {
		if v_0.Op != OpARM64MOVDconst {
			break
		}
		c := auxIntToInt64(v_0.AuxInt)
		if v_1.Op != OpARM64MOVDconst {
			break
		}
		d := auxIntToInt64(v_1.AuxInt)
		if !(d != 0) {
			break
		}
		v.reset(OpARM64MOVDconst)
		v.AuxInt = int64ToAuxInt(int64(uint32(int32(c) / int32(d))))
		return true
	}
	return false

`[a b c d a b c d]`.

>>> a = tf.constant([[1,2,3],[4,5,6]], tf.int32)
>>> b = tf.constant([1,2], tf.int32)
>>> tf.tile(a, b)
<tf.Tensor: shape=(2, 6), dtype=int32, numpy=
array([[1, 2, 3, 1, 2, 3],
       [4, 5, 6, 4, 5, 6]], dtype=int32)>
>>> c = tf.constant([2,1], tf.int32)
>>> tf.tile(a, c)
<tf.Tensor: shape=(4, 3), dtype=int32, numpy=
array([[1, 2, 3],
       [4, 5, 6],
       [1, 2, 3],

            "format": "int32",
            "type": "integer"
          },
          "revisionHistoryLimit": {
            "description": "The number of old history to retain to allow rollback. This is a pointer to distinguish between explicit zero and not specified. Defaults to 10.",
            "format": "int32",
            "type": "integer"
          },
          "selector": {

		}
	}
}

func TestValidateReplicationControllerStatus(t *testing.T) {
	tests := []struct {
		name string

		replicas             int32
		fullyLabeledReplicas int32
		readyReplicas        int32
		availableReplicas    int32
		observedGeneration   int64

		expectedErr bool
	}{{
		name:                 "valid status",
		replicas:             3,

        tf.TensorSpec([], dtypes.int32) ]) def _remote_multiply(a, b): return
        tf.math.multiply(a, b)

        server.register("multiply", _remote_multiply) ```

    *   Example usage to create client: `python client =
        tf.distribute.experimental.rpc.Client.create("grpc", address) a =
        tf.constant(2, dtype=tf.int32) b = tf.constant(3, dtype=tf.int32)