return argumentOp->getOperand(0);
  }

  return {};
}

}  // namespace detail

// This class adds property that the operation is idempotent.
// This means a unary to unary operation "f" that satisfies f(f(x)) = f(x),
// or a binary operation "g" that satisfies g(x, x) = x.
template <typename ConcreteType>
class IsIdempotent : public TraitBase<ConcreteType, IsIdempotent> {
 public:

}

// TODO(b/158596585): Replace this with a cost model analysis.
bool IsTrivialUnaryOperation(Operation* op) {
  return llvm::isa<TF::CastOp, TF::IdentityOp>(op);
}

// Adds outside compilation attributes to unary ops such as Identity/Cast ops
// at the head of TPU computation that is used only by other outside compiled
// ops. Identity ops and Cast ops is commonly added to the start of TPU

  if (op->getNumOperands() != 1) {
    return rewriter.notifyMatchFailure(
        op, "Function only supports unary einsum op");
  }
  RankedTensorType lhs =
      mlir::dyn_cast_or_null<RankedTensorType>(op.getOperand(0).getType());
  if (!lhs) {
    return failure();
  }
  // unary einsum op is only supported to the case where the operation can be
  // replaced using reduce_sum and/or transpose

            // - Labels themselves: `@label` in return`@label` or `label@`while...
            is KtLabelReferenceExpression ->
                false

            // - The operation symbol itself in binary and unary operations: `!!`, `+`...
            is KtOperationReferenceExpression ->
                false

            // All other expressions are used if their parent expression uses them.
            else ->

        return success();
      }
    }
    return op->emitOpError("Block has no predecessor");
  }

  if (v.getDefiningOp()->getNumOperands() == 1) {
    // If the value is originating from an unary op, assume it's something
    // simple like "cast" and keep tracing.
    return traceUpwardsToArgument(v.getDefiningOp()->getOperand(0), seen, out);
  } else {
    // Typically a tf.VarHandle op.

		n.Op.goString(indent+2, "Op: "))
}

// Unary is a unary operation in an expression.
type Unary struct {
	Op         AST
	Expr       AST
	Suffix     bool // true for ++ -- when used as postfix
	SizeofType bool // true for sizeof (type)
}

func (u *Unary) print(ps *printState) {
	op, _ := u.Op.(*Operator)
	expr := u.Expr

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

func (check *Checker) unary(x *operand, e *syntax.Operation) {
	check.expr(nil, x, e.X)
	if x.mode == invalid {
		return
	}

	op := e.Op
	switch op {
	case syntax.And:
		// spec: "As an exception to the addressability

  }

  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(

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: