const bool symmetric) {
  const float eps = 1e-7;
  ASSERT_THAT(*float_quant_params.min(), SizeIs(1));
  ASSERT_THAT(*float_quant_params.max(), SizeIs(1));
  float float_min = std::min(0.f, float_quant_params.min()->Get(0));
  float float_max = std::max(0.f, float_quant_params.max()->Get(0));
  if (symmetric) {
    // When the symmetric case, ConvertStatsToQDQs in PrepareQuantizePass

  // NO_FALLBACK: tf.MirrorPad
  // SUPPORTED_FALLBACK_DEVICE-NOT: tf.MirrorPad
  // UNSPECIFIED_FALLBACK_DEVICE: tf.MirrorPad
  // UNSUPPORTED_FALLBACK_DEVICE: tf.MirrorPad
  %1 = "tf.MirrorPad"(%arg0, %0) {mode = "SYMMETRIC"} : (tensor<2x3xcomplex<f64>>, tensor<2x2xi32>) -> tensor<4x7xcomplex<f64>>
  func.return %1 : tensor<4x7xcomplex<f64>>
}

// BatchMatMulV2 has native as well as fallback lowering patterns available.

		arg = argNN{a.z, a.y, a.x}
		testFunNN(t, "add symmetric", nat.add, arg)

		arg = argNN{a.x, a.z, a.y}
		testFunNN(t, "sub", nat.sub, arg)

		arg = argNN{a.y, a.z, a.x}
		testFunNN(t, "sub symmetric", nat.sub, arg)
	}

	for _, a := range prodNN {
		arg := a
		testFunNN(t, "mul", nat.mul, arg)

		arg = argNN{a.z, a.y, a.x}
		testFunNN(t, "mul symmetric", nat.mul, arg)
	}
}

    // When `disable_per_channel_` is false, per-channel symmetric quantization
    // parameters are created from the weights when the ops support per-channel
    // quantization. Otherwise, uses per-tensor asymmetric quantization with
    // narrow range.

    // per-axis quantization weight, with symmetric min/max enforced.
    final_type = GetUniformQuantizedPerAxisTypeForWeight(

  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
  auto paddings = Const(scope_, {{1, 1}, {2, 2}});
  TensorShape y_shape({4, 7});
  auto y = MirrorPad(scope_, x, paddings, "SYMMETRIC");
  RunTest(x, x_shape, y, y_shape);
}

TEST_F(ArrayGradTest, MirrorPadGradGrad_Reflect) {
  TensorShape x_shape({4, 7});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));

    there's any, and set it to True. The reason behind this decision is that
    generally activations of these ops show better accuracy with asymmetric
    input quantization so we want to deprecate symmetric activation quantization
    for those ops eventually.
    - Unlike to the old quantizer, per-channel quantization is supported for
    weight-only TransposeConvOp.
  }];

  let methods = [
    InterfaceMethod<

};

// Matches the pattern for quantized convolution op and rewrites it to use
// uniform quantized types.
//
// Currently assumes asymmetric per-tensor quantization for activations and
// symmetric per-channel quantization for filters.
//
// This pattern represents the following derived equation, where:
// * rn = real (expressed) value for tensor n
// * qn = quantized value for tensor n

		if _, ok := hpke.SupportedAEADs[s.AEADID]; !ok {
			continue
		}
		if _, ok := hpke.SupportedKDFs[s.KDFID]; !ok {
			continue
		}
		return s, nil
	}
	return echCipher{}, errors.New("tls: no supported symmetric ciphersuites for ECH")
}

func encodeInnerClientHello(inner *clientHelloMsg, maxNameLength int) ([]byte, error) {
	h, err := inner.marshalMsg(true)
	if err != nil {
		return nil, err
	}

      // Computes the effective min/max values of the attribute values.
      quant::ExtractMinMaxFromAttr(attr, /*dim_size=*/1, /*slice_size=*/1,
                                   /*symmetric=*/true, mins, maxs);
      double scale = maxs[0] / -llvm::minIntN(tensor_property.number_of_bits);
      quant_type = UniformQuantizedType::getChecked(
          const_op->getLoc(), quant::QuantizationFlags::Signed,

    }

    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(