func (s *Scalar) Add(x, y *Scalar) *Scalar {
	// s = 1 * x + y mod l
	fiatScalarAdd(&s.s, &x.s, &y.s)
	return s
}

// Subtract sets s = x - y mod l, and returns s.
func (s *Scalar) Subtract(x, y *Scalar) *Scalar {
	// s = -1 * y + x mod l
	fiatScalarSub(&s.s, &x.s, &y.s)
	return s
}

// Negate sets s = -x mod l, and returns s.
func (s *Scalar) Negate(x *Scalar) *Scalar {
	// s = -1 * x + 0 mod l

// metricValue is a runtime copy of runtime/metrics.Sample and
// must be kept structurally identical to that type.
type metricValue struct {
	kind    metricKind
	scalar  uint64         // contains scalar values for scalar Kinds.
	pointer unsafe.Pointer // contains non-scalar values.
}

// float64HistOrInit tries to pull out an existing float64Histogram
// from the value, but if none exists, then it allocates one with

      t.scalar<tensorflow::ResourceHandle>()().set_name("var1");
      t.scalar<tensorflow::ResourceHandle>()().set_device(kDeviceName);

      outputs->push_back(t);
    } else if (output_name == "var2") {
      Tensor t = Tensor(tensorflow::DT_RESOURCE, tensorflow::TensorShape({1}));
      t.scalar<tensorflow::ResourceHandle>()().set_name("var2");

    .Attr("max_percentile: float = 0.0")
    .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
      c->set_output(0, c->input(0));
      c->set_output(1, c->Scalar());
      c->set_output(2, c->Scalar());

      const tensorflow::AttrValue* num_bins_attr;
      TF_RETURN_IF_ERROR(c->GetAttr("num_bins", &num_bins_attr));
      c->set_output(3, c->MakeShape({num_bins_attr->i()}));

	return q.Set(&double)
}

// ScalarBaseMult sets r = scalar * generator, where scalar is a 32-byte big
// endian value, and returns r. If scalar is not 32 bytes long, ScalarBaseMult
// returns an error and the receiver is unchanged.
func (r *P256Point) ScalarBaseMult(scalar []byte) (*P256Point, error) {
	if len(scalar) != 32 {
		return nil, errors.New("invalid scalar length")
	}
	scalarReversed := new(p256OrdElement)

		panic("Uint64 called on Value of a different Kind")
	}
	return v.scalar
}

// valueAsString produces a debug string value.
//
// This isn't just Value.String because we may want to use that to store
// string values in the future.
func valueAsString(v Value) string {
	switch v.Kind() {
	case ValueUint64:
		return fmt.Sprintf("Uint64(%d)", v.scalar)
	}
	return "Bad"

					{Name: string(v1.ResourceCPU)},
					{Name: "scalar.io/scalar0"},
					{Name: "scalar.io/scalar1", Weight: 1},
				},
			},
			want: &configv1.NodeResourcesBalancedAllocationArgs{
				Resources: []configv1.ResourceSpec{
					{Name: string(v1.ResourceCPU), Weight: 1},
					{Name: "scalar.io/scalar0", Weight: 1},
					{Name: "scalar.io/scalar1", Weight: 1},
				},
			},
		},
		{

tflite::TensorType ConvertTypeToTensorType(mlir::Type type);

// Convert the scalar type of a TFlite tensor to the corresponding MLIR type.
mlir::Type ConvertElementType(tflite::TensorType type, mlir::Builder builder);

// Convert the scalar type of a TFLite tensor to the corresponding
// Tensorflow type
tensorflow::DataType TflTypeToTfType(tflite::TensorType type);

// Convert the Tensorflow scalar type to the corresponding TFLite type

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  TF_ImportGraphDefOptionsAddInputMapping(opts, "scalar", 0, {scalar, 0});
  TF_ImportGraphDefOptionsAddReturnOutput(opts, "feed", 0);
  TF_ImportGraphDefOptionsAddReturnOutput(opts, "scalar", 0);
  EXPECT_EQ(2, TF_ImportGraphDefOptionsNumReturnOutputs(opts));
  TF_ImportGraphDefOptionsAddReturnOperation(opts, "scalar");
  EXPECT_EQ(1, TF_ImportGraphDefOptionsNumReturnOperations(opts));