//   args {
//     dtype: DT_INT32
//     shape {
//       dim {
//         size: 8
//       }
// .   }
//     kind: PARAMETER
//     sharding {
//       type: MAXIMAL
//       tile_assignment_dimensions: 1
//       tile_assignment_devices: 0
//     }
//   }
//   retvals {
//     sharding {
//       type: MAXIMAL
//       tile_assignment_dimensions: 1

  else
    return UnrankedTensorType::get(type.getValue());
}
}  // namespace

// Returns whether type can be further refined.
bool CanBeRefined(Type type) {
  auto shape_type = mlir::dyn_cast<ShapedType>(type);
  if (!shape_type) return false;

  // Returns whether type with subtypes can be further refined.

    }
    if (ftype.isF64()) {
      return tflite::TensorType_COMPLEX128;
    }
    return Status(absl::StatusCode::kInvalidArgument, "Unsupported type");
  } else if (auto itype = mlir::dyn_cast<mlir::IntegerType>(type)) {
    switch (itype.getWidth()) {
      case 1:
        return tflite::TensorType_BOOL;
      case 4:
        if (itype.isUnsigned()) {
          return Status(absl::StatusCode::kInvalidArgument,

      T41, T42, T43, T44, T45, T46, T47, T48, T49> type;
};

namespace internal {

# define GTEST_TEMPLATE_ template <typename T> class

// The template "selector" struct TemplateSel<Tmpl> is used to
// represent Tmpl, which must be a class template with one type
// parameter, as a type.  TemplateSel<Tmpl>::Bind<T>::type is defined
// as the type Tmpl<T>.  This allows us to actually instantiate the

      T41, T42, T43, T44, T45, T46, T47, T48, T49> type;
};

namespace internal {

# define GTEST_TEMPLATE_ template <typename T> class

// The template "selector" struct TemplateSel<Tmpl> is used to
// represent Tmpl, which must be a class template with one type
// parameter, as a type.  TemplateSel<Tmpl>::Bind<T>::type is defined
// as the type Tmpl<T>.  This allows us to actually instantiate the

// TFLite dialect quint8 type - uses the TF quint8 type as implementation
//===----------------------------------------------------------------------===//
def TFL_Quint8 : Type<CPred<"$_self.isa<mlir::TF::Quint8Type>()">,
                    "TFLite quint8 type">,
              BuildableType<"getType<mlir::TF::Quint8Type>()">;

//===----------------------------------------------------------------------===//
// Type that represents control dependencies

                    Attribute value) {
  // Handle the case where the type and value are already tensors.
  if (mlir::isa<TensorType>(type) && mlir::isa<ElementsAttr>(value)) {
    result.addTypes(type);
    result.addAttribute("value", value);
    return;
  }

  // Otherwise, default to the attribute builder.
  ConstOp::build(builder, result, value);

				MetricV2{
					Description: MetricDescription{
						Namespace: bucketMetricNamespace,
						Subsystem: "batch",
						Name:      MetricName(jtype + "_objects"),
						Help:      "Get successfully completed batch job " + jtype + "objects",
						Type:      counterMetric,
					},
					Value:          objects,
					VariableLabels: map[string]string{"bucket": bucket, "jobId": mj.JobID},
				},
				MetricV2{

// for the specified type. That is, the returned Value's Type is [PointerTo](typ).
func New(typ Type) Value {
	if typ == nil {
		panic("reflect: New(nil)")
	}
	t := &typ.(*rtype).t
	pt := ptrTo(t)
	if pt.IfaceIndir() {
		// This is a pointer to a not-in-heap type.
		panic("reflect: New of type that may not be allocated in heap (possibly undefined cgo C type)")
	}
	ptr := unsafe_New(t)

		// node graceful shutdown). We do not re-generate the conditions based
		// on the container statuses as they are added based on one-time events.
		cType := v1.DisruptionTarget
		if _, condition := podutil.GetPodConditionFromList(oldPodStatus.Conditions, cType); condition != nil {
			s.Conditions = utilpod.ReplaceOrAppendPodCondition(s.Conditions, condition)
		}
	}

	// set all Kubelet-owned conditions