// admission controller. It rejects any pod that specifies "hard"
// (RequiredDuringScheduling) anti-affinity with a TopologyKey other
// than v1.LabelHostname.  Because anti-affinity is symmetric, without
// this admission controller, a user could maliciously or accidentally
// specify that their pod (once it has scheduled) should block other
// pods from scheduling into the same zone or some other large

// i.e. [-128, 127]. Assumes asymmetric quantization, meaning the zero point
// value can be a non-zero value.
// If `narrow_range` is set true (ex: for weights), a restricted range of
// integers will be used for symmetric mapping, i.e. [-127, 127].
UniformQuantizedType CreateI8F32UniformQuantizedType(Location loc,
                                                     MLIRContext& context,

		arg = argVV{a.z, a.y, a.x, a.c}
		testFunVV(t, "addVV_g symmetric", addVV_g, arg)
		testFunVV(t, "addVV symmetric", addVV, arg)

		arg = argVV{a.x, a.z, a.y, a.c}
		testFunVV(t, "subVV_g", subVV_g, arg)
		testFunVV(t, "subVV", subVV, arg)

		arg = argVV{a.y, a.z, a.x, a.c}
		testFunVV(t, "subVV_g symmetric", subVV_g, arg)
		testFunVV(t, "subVV symmetric", subVV, arg)
	}
}

    public abstract fun restoreSymbol(analysisSession: KaSession): S?

    /**
     * @return **true** if [other] pointer can be restored to the same symbol. The operation is symmetric and transitive.
     */
    public open fun pointsToTheSameSymbolAs(other: KaSymbolPointer<KaSymbol>): Boolean = this === other

    override fun toString(): String = renderAsDataClassToString()
}

    Type weight_type;
    if (IsPerTensor(weight_only_ptq)) {
      weight_type = dyn_cast<quant::QuantizedType>(
          quant::GetUniformQuantizedTypeForWeight(
              attr, /*symmetric=*/true, /*num_bits=*/8, /*is_signed=*/true,
              /*narrow_range=*/true, /*legacy_float_scale=*/false));
    } else {
      int quantization_dimension = GetQuantizationDimension(

		testRatBin(t, i, "Add", (*Rat).Add, arg)

		arg = ratBinArg{test.y, test.x, test.sum}
		testRatBin(t, i, "Add symmetric", (*Rat).Add, arg)

		arg = ratBinArg{test.sum, test.x, test.y}
		testRatBin(t, i, "Sub", (*Rat).Sub, arg)

		arg = ratBinArg{test.sum, test.y, test.x}
		testRatBin(t, i, "Sub symmetric", (*Rat).Sub, arg)

		arg = ratBinArg{test.x, test.y, test.prod}
		testRatBin(t, i, "Mul", (*Rat).Mul, arg)

    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

    PatternRewriter& rewriter, TF::ConstOp op,
    tensorflow::quantization::QuantizationComponentSpec& weight_spec) {
  // TODO - b/278949920: Enable Per-Channel Quantization for XLA Opset
  // Currently, support symmetric, per-tensor, signed int8
  const bool kIsNarrowRange = true;
  const bool kIsSigned = true;
  const int kBitWidth = 8;

  DenseFPElementsAttr attr;

  // 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.

	_[T interface{ int|T /* ERROR "term cannot be a type parameter" */ }] struct{}
)

// Multiple embedded union elements are intersected. The order in which they
// appear in the interface doesn't matter since intersection is a symmetric
// operation.

type myInt1 int
type myInt2 int

func _[T interface{ myInt1|myInt2; ~int }]() T { return T(0) }
func _[T interface{ ~int; myInt1|myInt2 }]() T { return T(0) }