// Here, the element type can be any integer or float type. But, note that only
// 32 bit integers are supported for the values.
class IsConstantValueOf<int value> : Constraint<CPred<
  "IsConstantValueOf($0," # value # ")">>;

def IsOnGpuDevicePred : CPred<"IsOnGpuDevice($0.getOwner())">;

    }
  }
  llvm_unreachable("unsupported type");
}

// Returns true if `value` is compile-time constant and its splat value equals
// to `raw_value`.
template <typename T>
bool IsConstantValueOf(Value value, T raw_value) {
  auto element_type = mlir::cast<ShapedType>(value.getType()).getElementType();
  if (mlir::isa<FloatType>(element_type)) {
    DenseFPElementsAttr float_attr;

// canonicalization device independent, by adding an int32 GPU kernel for Relu.
def MaximumOfZeroToRelu : Pat<
  (TF_MaximumOp:$maximum_op $x, $y), (TF_ReluOp:$dest $x),
  [(IsConstantValueOf<0> $y), (IsDeviceCompatible $maximum_op)],
  [(CopyAttrs $maximum_op, $dest)]>;

//===----------------------------------------------------------------------===//
// Canonicalize tf.Relu of Minimul six to tf.Relu6

  (TFL_ReshapeOp $input, (Arith_ConstantOp (GetShape $expand_dims_op))),
  [(AnyStaticShapeTensor $expand_dims_op)]>;

// Here, the element type can be any integer or float type.
class IsConstantValueOf<int value> : Constraint<CPred<
  "TFL::IsConstantValueOf($0," # value # ")">>;

// ReLU patterns
def MatchReluPattern : Pat<
  (TFL_MaximumOp $input, (Arith_ConstantOp $Zero)),
  (TFL_ReluOp $input),
  [(FloatValueEquals<"0"> $Zero)]>;

    return f.isExactlyValue(value);
  });
}

// Returns true if `value` is compile-time constant and its splat value equals
// to `raw_value`.
template <typename T>
bool IsConstantValueOf(mlir::TypedAttr value, T raw_value) {
  auto element_type = mlir::cast<ShapedType>(value.getType()).getElementType();

  if (mlir::isa<FloatType>(element_type)) {
    return FloatValueEquals(value, raw_value);