op_VCHL   uint32 = 0xE7F9 // 	VRR-b	VECTOR COMPARE HIGH LOGICAL
	op_VCLZ   uint32 = 0xE753 // 	VRR-a	VECTOR COUNT LEADING ZEROS
	op_VCTZ   uint32 = 0xE752 // 	VRR-a	VECTOR COUNT TRAILING ZEROS
	op_VEC    uint32 = 0xE7DB // 	VRR-a	VECTOR ELEMENT COMPARE
	op_VECL   uint32 = 0xE7D9 // 	VRR-a	VECTOR ELEMENT COMPARE LOGICAL
	op_VERIM  uint32 = 0xE772 // 	VRI-d	VECTOR ELEMENT ROTATE AND INSERT UNDER MASK

		return true
	}
	return sequenceOfOnes(x) || sequenceOfOnes(^x)
}

// sequenceOfOnes tests whether a constant is a sequence of ones in binary, with leading and trailing zeros.
func sequenceOfOnes(x uint64) bool {
	y := x & -x // lowest set bit of x. x is good iff x+y is a power of 2
	y += x
	return (y-1)&y == 0
}

          </association>
        </field>
      </fields>
      <comment>We could probably have a specific element for a dev mailing list for things like CI,
        and maybe even a specific element for the user and scm mailing lists. Then leave the more
        lose structure for any other type of mailing list.</comment>
      <codeSegments>
        <codeSegment>
          <version>4.0.0+</version>
          <code>

    public static final String ERRORS_design_file_is_unsupported_type = "{errors.design_file_is_unsupported_type}";

    /** The key of the message: Failed to create a crawling config. */
    public static final String ERRORS_failed_to_create_crawling_config_at_wizard = "{errors.failed_to_create_crawling_config_at_wizard}";

    /** The key of the message: This feature is disabled. */

from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import signature_def_utils_impl
from tensorflow.python.saved_model import tag_constants
from tensorflow.python.training import checkpoint_utils
from tensorflow.python.types import core


# Type aliases for quantization method protobuf enums.
_PresetMethod = quant_opts_pb2.QuantizationMethod.PresetMethod
_CalibrationMethod = (

  // TODO(ezhulenev): This might lead to excessive transposes in the final IR,
  // if the ratio of 1x1 convolutions to regular convolutions is close to 1:1.
  // Also FusedBatchNorm in training mode prefers NCHW data format. Check if all
  // users can efficiently use NHWC data format?
  if (one_by_one && trivial_strides && trivial_dilations) {
    return "NHWC";
  }

			secretKey:        credentials.SecretKey,

			expectedRespStatus: http.StatusBadRequest,
		},

		// Test case - 8.
		// Test case with non-existent source file.
		// Case for the purpose of failing `api.ObjectAPI.GetObjectInfo`.
		// Expecting the response status code to http.StatusNotFound (404).
		{
			bucketName:       bucketName,
			uploadID:         uploadID,

  let summary = "Updates inputs to TPU embedding enqueue ops depending on whether graph is in training mode or in evaluation mode.";

  let description = [{
    Updates inputs to TPU embedding enqueue ops depending on whether graph
    is in training mode or in evaluation mode.
  }];

  let constructor = "TFTPU::CreateTPUUpdateEmbeddingEnqueueOpInputsPass()";
}

		case images.ErrImagePullBackOff.Error():
			return kubecontainer.ContainerID{}, fmt.Errorf("container %q in pod %q is waiting to start: trying and failing to pull image", containerName, podName)
		default:
			return kubecontainer.ContainerID{}, fmt.Errorf("container %q in pod %q is waiting to start: %v", containerName, podName, reason)
		}
	default:

func TestSchedulerGuaranteeNonNilNodeInSchedulingCycle(t *testing.T) {
	if goruntime.GOOS == "windows" {
		// TODO: remove skip once the failing test has been fixed.
		t.Skip("Skip failing test on Windows.")
	}
	random := rand.New(rand.NewSource(time.Now().UnixNano()))
	ctx, cancel := context.WithCancel(context.Background())
	defer cancel()

	var (