}

  const std::string type_index_name =
      ::tensorflow::port::MaybeAbiDemangle(a.TypeId().name());

  return ::tensorflow::errors::Internal(
      "No unary variant binary_op function found for op ADD Variant "
      "type_name: ",
      type_index_name, " for device type: ", cc_ctx->device()->name());
}

void TF_AddNVariant(TF_OpKernelContext* ctx,

            ConstantValueKind.Boolean -> CompileTimeType.BOOLEAN
            ConstantValueKind.String -> CompileTimeType.STRING

            else -> CompileTimeType.ANY
        }
    }

    // Unary operators
    private fun FirLiteralExpression.evaluate(function: FirSimpleFunction): FirLiteralExpression? {
        if (value == null) return null
        (value as? String)?.let { opr ->
            evalUnaryOp(

                                             #v1, \
                                             pred, \
                                             v1), on_failure)

// Unary predicate assertion macros.
#define EXPECT_PRED_FORMAT1(pred_format, v1) \
  GTEST_PRED_FORMAT1_(pred_format, v1, GTEST_NONFATAL_FAILURE_)
#define EXPECT_PRED1(pred, v1) \
  GTEST_PRED1_(pred, v1, GTEST_NONFATAL_FAILURE_)

// example tf.Sub operation.
def TF_CwiseBinary : NativeOpTrait<"TF::CwiseBinary">;

// Coefficient wise unary operation, for example tf.Sqrt operation.
def TF_CwiseUnary : NativeOpTrait<"TF::CwiseUnary">;

// op op X == op X (unary) / X op X == X (binary)
// This version requires TF_SameOperandsAndResultTypeResolveRef trait unlike
// the core implementation requiring SameOperandsAndResultType.

namespace mlir::quant {

using QuantMethod = tensorflow::quantization::QuantizationMethod::PresetMethod;

enum class OpType {
  kDynamicRangeOp,  // Dynamic Range kernels only have rhs attr.
  kUnaryOp,         // Unary ops have one min/max attr.
  kBinaryOp,        // Binary ops have lhs/rhs attr.
  kQuantizationOp,  // Quantization ops have input/output attr.
};

// For each op type, the following axis carries axis information:

                                             #v1, \
                                             pred, \
                                             v1), on_failure)

// Unary predicate assertion macros.
#define EXPECT_PRED_FORMAT1(pred_format, v1) \
  GTEST_PRED_FORMAT1_(pred_format, v1, GTEST_NONFATAL_FAILURE_)
#define EXPECT_PRED1(pred, v1) \
  GTEST_PRED1_(pred, v1, GTEST_NONFATAL_FAILURE_)

        file("build/reports/pmd/test.xml").assertContents(containsClass("org.gradle.Class1Test"))
        output.contains "\tUsing multiple unary operators may be a bug"
        output.contains "\tEnsure you override both equals() and hashCode()"
    }

    void "can configure number of threads on good code"() {
        goodCode()
        buildFile << """

	}

	// A StarExpr node represents an expression of the form "*" Expression.
	// Semantically it could be a unary "*" expression, or a pointer type.
	//
	StarExpr struct {
		Star token.Pos // position of "*"
		X    Expr      // operand
	}

	// A UnaryExpr node represents a unary expression.
	// Unary "*" expressions are represented via StarExpr nodes.
	//
	UnaryExpr struct {
		OpPos token.Pos   // position of Op

	typ = Unalias(typ)
	if tpar, _ := typ.(*TypeParam); tpar != nil {
		return tpar.underIs(f)
	}
	return f(under(typ))
}

// The unary expression e may be nil. It's passed in for better error messages only.
func (check *Checker) unary(x *operand, e *ast.UnaryExpr) {
	check.expr(nil, x, e.X)
	if x.mode == invalid {
		return
	}

	op := e.Op
	switch op {
	case token.AND:

		arrow := p.pos
		p.next()

		// If the next token is token.CHAN we still don't know if it
		// is a channel type or a receive operation - we only know
		// once we have found the end of the unary expression. There
		// are two cases:
		//
		//   <- type  => (<-type) must be channel type
		//   <- expr  => <-(expr) is a receive from an expression
		//