UpdateStrategy: appsv1beta2.StatefulSetUpdateStrategy{
						Type: appsv1beta2.RollingUpdateStatefulSetStrategyType,
						RollingUpdate: &appsv1beta2.RollingUpdateStatefulSetStrategy{
							Partition: &defaultPartition,
						},
					},
					RevisionHistoryLimit: ptr.To[int32](10),
				},
			},
		},
		{
			name: "Alternate update strategy",
			original: &appsv1beta2.StatefulSet{

  // Add TF->TF lowering patterns.
  TF::PopulateTFLoweringBeforeHLOPatterns(context, &legalize_lower_patterns);

  if (tf2xla_fallback_device_type && prefer_tf2xla) {
    VLOG(1) << "TF to XLA legalization patterns are partitioned by op into "
               "either native MLIR legalization, or TF2XLA fallback "
               "legalzation, with a preference toward TF2XLA.";
  } else if (tf2xla_fallback_device_type) {

};

TEST_F(DataFlowGradTest, DynamicPartitionGrad) {
  TensorShape data_shape({2, 3, 2});
  auto data = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(data_shape));
  auto partitions = Const(scope_, {{2, 1, 0}, {1, 2, 0}});
  auto y = DynamicPartition(scope_, data, partitions, 3);
  TensorShape partition_shape({2, 2});
  RunTest({data}, {data_shape}, y.outputs,
          {partition_shape, partition_shape, partition_shape});
}

				if err != nil {
					c.Expires = time.Time{}
					break
				}
			}
			c.Expires = exptime.UTC()
			continue
		case "path":
			c.Path = val
			continue
		case "partitioned":
			c.Partitioned = true
			continue
		}
		c.Unparsed = append(c.Unparsed, parts[i])
	}
	return c, nil
}

// readSetCookies parses all "Set-Cookie" values from

				Spec: appsv1beta1.StatefulSetSpec{
					Template: defaultTemplate,
					UpdateStrategy: appsv1beta1.StatefulSetUpdateStrategy{
						RollingUpdate: &appsv1beta1.RollingUpdateStatefulSetStrategy{
							Partition:      &defaultPartition,
							MaxUnavailable: ptr.To(intstr.FromInt32(1)),
						},
					},
				},
			},
			expected: &appsv1beta1.StatefulSet{
				Spec: appsv1beta1.StatefulSetSpec{

        from = partitionPoint + 1;
      }
    }
  }

  /**
   * Performs a partition operation on the slice of {@code array} with elements in the range [{@code
   * from}, {@code to}]. Uses the median of {@code from}, {@code to}, and the midpoint between them
   * as a pivot. Returns the index which the slice is partitioned around, i.e. if it returns {@code

    }

    def "partitioning"() {
        when:
        def pair = partition([1, 2, 3], { it % 2 == 0 })

        then:
        pair.left == [2]
        pair.right == [1, 3]
    }

    def "partitioning empty collection"() {
        when:
        def pair = partition([], { it })

        then:
        pair.left == []
        pair.right == []
    }

        "tf.StatefulPartitionedCall"(%arg0) <{config = "", config_proto = "", executor_type = "", f = @partitioned}> : (tensor<*x!tf_type.resource>) -> ()
        tf_executor.yield
      }
      tf_executor.fetch %control : !tf_executor.control
    }
    return
  }

def get_authorization_scheme_param(
    authorization_header_value: Optional[str],
) -> Tuple[str, str]:
    if not authorization_header_value:
        return "", ""
    scheme, _, param = authorization_header_value.partition(" ")

message RollingUpdateStatefulSetStrategy {
  // 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.
  optional int32 partition = 1;