}

    if (is_per_channel_quantization) {
      quant_type = mlir::dyn_cast<quant::QuantizedType>(
          quant::GetUniformQuantizedPerAxisTypeForWeight(
              attr, quant_dim,
              /*symmetric=*/true, bit_width, is_signed, is_narrow_range,
              is_legacy_float));
    } else {
      quant_type = mlir::dyn_cast<quant::QuantizedType>(
          quant::GetUniformQuantizedTypeForWeight(

    flatbuffers::FlatBufferBuilder* builder) {
  switch (padding) {
    case mlir::TFL::MirrorPaddingType::REFLECT:
      return tflite::MirrorPadMode_REFLECT;
    case mlir::TFL::MirrorPaddingType::SYMMETRIC:
      return tflite::MirrorPadMode_SYMMETRIC;
  }
  llvm_unreachable("invalid mirror_pad_enum in conversion.");
}

static tflite::TensorType ConvertDerivedTypeAttrForOptionWriter(

   *
   * <ul>
   *   <li>{@code equivalent(x, x)} is true (<i>reflexive</i> property)
   *   <li>{@code equivalent(x, y)} and {@code equivalent(y, x)} each return the same result
   *       (<i>symmetric</i> property)
   *   <li>If {@code equivalent(x, y)} and {@code equivalent(y, z)} are both true, then {@code
   *       equivalent(x, z)} is also true (<i>transitive</i> property)
   * </ul>
   *

 * 
 * <p>
 * Applications can use the methods <code>getLocalHost</code>,
 * <code>getByName</code>, and
 * <code>getAllByAddress</code> to create a new NbtAddress instance. This
 * class is symmetric with {@link java.net.InetAddress}.
 *
 * <p>
 * <b>About NetBIOS:</b> The NetBIOS name
 * service is a dynamic distributed service that allows hosts to resolve

		}
		blockSize *= 2
	}
}

// symMerge merges the two sorted subsequences data[a:m] and data[m:b] using
// the SymMerge algorithm from Pok-Son Kim and Arne Kutzner, "Stable Minimum
// Storage Merging by Symmetric Comparisons", in Susanne Albers and Tomasz
// Radzik, editors, Algorithms - ESA 2004, volume 3221 of Lecture Notes in
// Computer Science, pages 714-723. Springer, 2004.
//
// Let M = m-a and N = b-n. Wolog M < N.

      quant_type = mlir::dyn_cast<quant::QuantizedType>(
          quant::GetUniformQuantizedPerAxisTypeForWeight(
              attr, affine_user.GetQuantizationDimIndex(),
              /*symmetric=*/true, bit_width, is_signed, is_narrow_range,
              is_legacy_float));
    } else {
      quant_type = mlir::dyn_cast<quant::QuantizedType>(
          quant::GetUniformQuantizedTypeForWeight(

   *
   * <ul>
   *   <li>{@code equivalent(x, x)} is true (<i>reflexive</i> property)
   *   <li>{@code equivalent(x, y)} and {@code equivalent(y, x)} each return the same result
   *       (<i>symmetric</i> property)
   *   <li>If {@code equivalent(x, y)} and {@code equivalent(y, z)} are both true, then {@code
   *       equivalent(x, z)} is also true (<i>transitive</i> property)
   * </ul>
   *

rotations uses O(u + v + gcd(u,v)) assignments which is
better than our O(3 * (u+v)) as gcd(u,v) <= u.


Stable sorting by SymMerge and BlockSwap rotations

SymMerg complexity for same size input M = N:
Calls to Less:  O(M*log(N/M+1)) = O(N*log(2)) = O(N)
Calls to Swap:  O((M+N)*log(M)) = O(2*N*log(N)) = O(N*log(N))

(The following argument does not fuzz over a missing -1 or

    module_str = self._extract_first_xla_call_module_op(
        self._output_saved_model_path
    )

    # Tests that the output graph contains multiply for symmetric
    # dequantization.
    self.assertTrue(re.search('stablehlo.multiply', module_str))
    # Tests that the output graph contains float dot_general.
    self.assertTrue(

	for _, a := range sumZZ {
		arg := a
		testFunZZ(t, "AddZZ", AddZZ, arg)

		arg = argZZ{a.z, a.y, a.x}
		testFunZZ(t, "AddZZ symmetric", AddZZ, arg)

		arg = argZZ{a.x, a.z, a.y}
		testFunZZ(t, "SubZZ", SubZZ, arg)

		arg = argZZ{a.y, a.z, a.x}
		testFunZZ(t, "SubZZ symmetric", SubZZ, arg)
	}
}

func TestProdZZ(t *testing.T) {
	MulZZ := func(z, x, y *Int) *Int { return z.Mul(x, y) }