}
    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,

        xla_shape.dimensions(), &tensor_shape));
    arg_shapes.emplace_back(tensor_shape);

    DataType dtype;
    TF_RETURN_IF_ERROR(ConvertToDataType(input_type, &dtype));

    auto metadata_arg = metadata_proto.add_args();
    metadata_arg->set_kind(tpu::TPUCompileMetadataProto::Arg::PARAMETER);
    metadata_arg->set_dtype(dtype);
  }

  return absl::OkStatus();
}

absl::Status SetupReturnValues(mlir::ModuleOp module,

      OpT op, typename OpT::Adaptor adaptor,
      ConversionPatternRewriter &rewriter) const override {
    Type dtype = op.getElementDtype();
    if (!(dtype.isF16() || dtype.isF32() || dtype.isF64() ||
          dtype.isInteger(1) || dtype.isInteger(8) || dtype.isInteger(16) ||
          dtype.isInteger(32) || dtype.isInteger(64))) {
      const char *error_info =
          "requires element_dtype to be 1-bit/8-bit/16-bit/32-bit/64-bit "

for dtype in dtype_list:
  lhs = tf.constant([-1, -5, -3, -14], dtype=dtype)
  rhs = tf.constant([5, 0, 7, 11], dtype=dtype)

  right_shift_result = bitwise_ops.right_shift(lhs, rhs)

  print(right_shift_result)

# This will print:
# tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int8)
# tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int16)

func ensureJobConditionStatus(list []batch.JobCondition, cType batch.JobConditionType, status v1.ConditionStatus, reason, message string, now time.Time) ([]batch.JobCondition, bool) {
	if condition := findConditionByType(list, cType); condition != nil {
		if condition.Status != status || condition.Reason != reason || condition.Message != message {
			*condition = *newCondition(cType, status, reason, message, now)
			return list, true
		}

        the layer's dtype. If you had a model that used `float64`, it will
        probably silently use `float32` in TensorFlow2, and a warning will be
        issued that starts with Layer "layer-name" is casting an input tensor
        from dtype float64 to the layer's dtype of float32. To fix, either set
        the default dtype to float64 with

	if !ptr.Type.IsPtr() || !types.Identical(n.Type(), ptr.Type.Elem()) {
		base.FatalfAt(n.Pos(), "setHeapaddr %L with type %v", n, ptr.Type)
	}

	// Declare variable to hold address.
	sym := &types.Sym{Name: "&" + n.Sym().Name, Pkg: types.LocalPkg}
	addr := s.curfn.NewLocal(pos, sym, types.NewPtr(n.Type()))
	addr.SetUsed(true)
	types.CalcSize(addr.Type())

	if n.Class == ir.PPARAMOUT {

		// 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

  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.

				for _, wantCondition := range *tc.wantConditions {
					conditions := getConditionsByType(actual.Status.Conditions, wantCondition.Type)
					if len(conditions) != 1 {
						t.Fatalf("Expected a single completion condition. Got %#v for type: %q", conditions, wantCondition.Type)
					}
					condition := *conditions[0]