n := n.(*ir.SendStmt)
			n.Value = typecheck.NodAddr(n.Value)
			n.Value = typecheck.Expr(n.Value)

		case ir.OSELRECV2:
			n := n.(*ir.AssignListStmt)
			if !ir.IsBlank(n.Lhs[0]) {
				n.Lhs[0] = typecheck.NodAddr(n.Lhs[0])
				n.Lhs[0] = typecheck.Expr(n.Lhs[0])
			}
		}
	}

	// optimization: two-case select but one is default: single non-blocking op.
	if ncas == 2 && dflt != nil {
		cas := cases[0]
		if cas == dflt {

			Bool:       true,
			Structural: &stringSchema,
		},
	}
)

func TestEquality(t *testing.T) {
	cases := []struct {
		name  string
		lhs   ref.Val
		rhs   ref.Val
		equal bool
	}{
		{
			name: "map lists are equal regardless of order",
			lhs: UnstructuredToVal([]interface{}{
				map[string]interface{}{
					"key": "a",
					"val": 1,
				},
				map[string]interface{}{
					"key": "b",

// CHECK-LABEL:   func @batchmatmulv2_adj_complex(
// CHECK-SAME:                                    [[LHS:%.*]]: tensor<2x5xcomplex<f32>>, [[RHS:%.*]]: tensor<4x2xcomplex<f32>>) -> tensor<5x4xcomplex<f32>> {
// CHECK:           [[LHSRE:%.*]] = mhlo.real [[LHS]]
// CHECK:           [[LHSIM:%.*]] = mhlo.imag [[LHS]]
// CHECK:           [[LHSIMNEG:%.*]] = mhlo.negate [[LHSIM]]

def LegalizeMul : Pat<(TF_MulOp $lhs, $rhs),
                      (TFL_MulOp $lhs, $rhs, TFL_AF_None)>;
def LegalizeRealDiv : Pat<(TF_RealDivOp $lhs, $rhs),
                          (TFL_DivOp $lhs, $rhs, TFL_AF_None)>;
def LegalizeDiv : Pat<(TF_DivOp $lhs, $rhs),
                      (TFL_DivOp $lhs, $rhs, TFL_AF_None)>;

// When batch size is known, TF BatchMatMul gets unfolded to TFL FullyConnected

	t3 := Must(t0.Clone())
	Must(t3.Parse(`{{define "lhs"}} <style> {{end}}`))
	Must(t3.Parse(`{{define "rhs"}} </style> {{end}}`))

	// Complete t0.
	Must(t0.Parse(`{{define "lhs"}} ( {{end}}`))
	Must(t0.Parse(`{{define "rhs"}} ) {{end}}`))

	// Clone t0 as t4. Redefining the "lhs" template should not fail.
	t4 := Must(t0.Clone())
	if _, err := t4.Parse(`{{define "lhs"}} OK {{end}}`); err != nil {

def LiftConv : Pat<
  (StableHLO_ConvolutionOp:$res $lhs, $rhs, $window_strides, $padding,
      $lhs_dilation, $rhs_dilation, $window_reversal, $dimension_numbers,
      $feature_group_count, $batch_group_count, $precision_config),
  (LiftAsTFXlaCallModule<"composite_conv_fn">
    (ArgumentList $lhs, $rhs),
    (ResultList $res),
    (NamedAttributeList

      input.getLoc(), input, rewriter.getDenseI64ArrayAttr(permutation));
}

// Defers the transpose of the left-hand side (LHS) to the right-hand side and
// the result of a binary operation. In detail, this rewrites the
// `op(transpose(%rhs), %lhs)` to `transpose(op(%rhs, transpose(%lhs)))`. The
// LHS transpose permutation must be a NCHW->NHWC permutation.
template <typename OpT>

}  // namespace tensorflow

#define TF_STATUS_ASSIGN_OR_RETURN(lhs, rexpr, c_status) \
  _TF_STATUS_ASSIGN_OR_RETURN_IMPL(                      \
      _TF_STATUS_CONCAT(_status_or_value, __COUNTER__), lhs, rexpr, c_status);

#define _TF_STATUS_ASSIGN_OR_RETURN_IMPL(statusor, lhs, rexpr, c_status) \
  auto statusor = (rexpr);                                               \

                    lhs = PropertyAccess [indexes: 0..1, line/column: 1/1..1/2, file: test] (
                        name = a
                    )
                    rhs = IntLiteral [indexes: 4..5, line/column: 1/5..1/6, file: test] (1)
                )
                Assignment [indexes: 6..16, line/column: 2/1..2/11, file: test] (

    }

    private static boolean scopeEquals(String lhs, String rhs) {
        if ("COMPILE".equals(lhs)) {
            return Strings.isNullOrEmpty(rhs) || "COMPILE".equals(rhs);
        } else if ("COMPILE".equals(rhs)) {
            return Strings.isNullOrEmpty(lhs);
        } else {
            return Objects.equal(lhs, rhs);
        }
    }

    @Override
    public int hashCode() {