func.func @DontReorderReshapex2Add(%arg0: tensor<1x2x3x4x!quant.uniform<i8:f32, 3.0>>, %arg1: tensor<1x2x3x4x!quant.uniform<i8:f32, 5.0>>) -> tensor<6x4x!quant.uniform<i8:f32, 7.0>> {
  %shape = arith.constant dense<[6, 4]> : tensor<2xi32>
  %0 = "tfl.reshape"(%arg0, %shape) : (tensor<1x2x3x4x!quant.uniform<i8:f32, 3.0>>, tensor<2xi32>) -> tensor<6x4x!quant.uniform<i8:f32, 3.0>>

}

// CHECK-LABEL:   func @const_quant
// CHECK-NOT:       "tf.Const"
func.func @const_quant() -> tensor<512x1x!quant.uniform<i8:f32, 0.013133913278579712>> {
  %0 = mhlo.constant() {value = dense<0> : tensor<512x1xi8>} : () -> tensor<512x1x!quant.uniform<i8:f32, 0.013133913278579712>>
  func.return %0 : tensor<512x1x!quant.uniform<i8:f32, 0.013133913278579712>>
}

// CHECK-LABEL:   func @convert_dot_quant_type(

      SrcOpT op, typename SrcOpT::Adaptor adaptor,
      ConversionPatternRewriter &rewriter) const override {
    DstOpT mhlo_op;
    Location loc = op.getLoc();

    // To handle quant type conversions, use the converted operands' element
    // types and original source op's shapes and encoding to get converted op's
    // result types. This is only done for the While op for now.