TFL_FullyConnectedOptionsWeightFormatAttr, TFL_FCWOEnum_Shuffled4x16i8.symbol>;

// MirrorPadding type attributes
def TFL_MIRRORPAD_Reflect : I32EnumAttrCase<"REFLECT", 0>;
def TFL_MIRRORPAD_Symmetric : I32EnumAttrCase<"SYMMETRIC", 1>;
def TFL_MirrorPaddingType : I32EnumAttr<"MirrorPaddingType", "mirror_pad_enum", [
      TFL_MIRRORPAD_Reflect, TFL_MIRRORPAD_Symmetric
    ]> {
  let genSpecializedAttr = 0;
  let cppNamespace = "::mlir::TFL";

		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)
	}
}

 * 
 * <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

    // 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(