//        |         |
    //      Const_0    Const_1
    //
    const float const0_val[] = {1.0, 2.0, 3.0, 4.0};
    const float const1_val[] = {1.0, 0.0, 0.0, 1.0};
    TF_Operation* const0 = FloatConst2x2(graph_, s_, const0_val, "Const_0");
    TF_Operation* const1 = FloatConst2x2(graph_, s_, const1_val, "Const_1");
    TF_Operation* matmul = MatMul(graph_, s_, const0, const1, "MatMul");

    auto elem_type = const_val.getElementType();
    Attribute result;
    if (mlir::isa<FloatType>(elem_type)) {
      result = ConstFoldBroadcastInDim<FloatAttr>(result_type, const_val,
                                                  bcast_dims);
    } else if (mlir::isa<IntegerType>(elem_type)) {
      result = ConstFoldBroadcastInDim<IntegerAttr>(result_type, const_val,

	if c {
		i = 1
	}
	return f.constVal(OpConstBool, t, i, true)
}
func (f *Func) ConstInt8(t *types.Type, c int8) *Value {
	return f.constVal(OpConst8, t, int64(c), true)
}
func (f *Func) ConstInt16(t *types.Type, c int16) *Value {
	return f.constVal(OpConst16, t, int64(c), true)
}
func (f *Func) ConstInt32(t *types.Type, c int32) *Value {
	return f.constVal(OpConst32, t, int64(c), true)
}

        if (val.getType() == t) return val;
        return rewriter.create<tensor::CastOp>(op.getLoc(), t, val);
      };
      last_val[0] = maybe_cast(const_val, op.getResult(3).getType());
      last_val[1] = maybe_cast(const_val, op.getResult(4).getType());
    }
    rewriter.replaceOp(
        op, {/*x_backprop=*/x_backprop,
             /*scale_backprop=*/scale_backprop,