PrioritizeVerb: "prioritize",
		BindVerb:       "bar",
		Weight:         1,
	})

	validPlugins := validConfig.DeepCopy()
	validPlugins.Profiles[0].Plugins.Score.Enabled = append(validPlugins.Profiles[0].Plugins.Score.Enabled, config.Plugin{Name: "PodTopologySpread", Weight: 2})

	invalidPlugins := validConfig.DeepCopy()

	border-width: 1px 0 0;
	clear: both;
	margin: 1.25em 0 1.1875em;
	height: 0;
}

/* Helpful Typography Defaults */
em,
i {
	font-style: italic;
	line-height: inherit;
}

strong,
b {
	font-weight: bold;
	line-height: inherit;
}

small {
	font-size: 60%;
	line-height: inherit;
}

code {
	font-family: "Droid Sans Mono", "DejaVu Sans Mono", monospace;

func.func private @conv(%input: tensor<1x3x4x3xf32> {tf._user_specified_name = "input_tensor"}) -> tensor<*xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<1x3x4x3>]} {
  %weight = arith.constant dense_resource<__elided__> : tensor<2x3x3x2xf32>
  %bias = arith.constant dense<[7.11401462, 7.05456924]> : tensor<2xf32>

// RUN: stablehlo-quant-opt %s -split-input-file -verify-diagnostics \
// RUN:     -stablehlo-quantize-composite-functions | FileCheck --check-prefix=CHECK %s

// Test that per-tensor weight-only quantized dot_general op is produced when
// empty `weight_only_ptq` is provided.

module attributes {tf_saved_model.semantics} {
  func.func private @quantize_dot_general_per_tensor(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> attributes {tf._original_func_name = "main_0"} {

          },
          "weight": {
            "default": 0,
            "description": "Weight associated with matching the corresponding nodeSelectorTerm, in the range 1-100.",
            "format": "int32",
            "type": "integer"
          }
        },
        "required": [
          "weight",
          "preference"
        ],

  static bool GetArithmeticCountForConvAndFullyconnectedOp(mlir::Operation* op,
                                                           int64_t* count) {
    auto weight = op->getOperand(1);
    auto weight_type =
        mlir::dyn_cast_or_null<mlir::RankedTensorType>(weight.getType());
    if (weight_type == nullptr || !weight_type.hasStaticShape()) return false;

    auto output = op->getResult(0);
    auto output_type =

				Weight:      55,
			}, {
				Destination: &networking.Destination{Host: "foo.baz.east"},
				Weight:      50,
			}},
		}, valid: true},
		{name: "total weight < 100", route: &networking.HTTPRoute{
			Route: []*networking.HTTPRouteDestination{{
				Destination: &networking.Destination{Host: "foo.baz.south"},
				Weight:      49,
			}, {

// be 63 bytes or fewer.
func (w SRVWriter) AddSRV(priority, weight, port uint16, target string) error {
	targetName, err := dnsmessage.NewName(target)
	if err != nil {
		return err
	}
	w.a.wrote = true
	err = w.a.builder.SRVResource(w.header(), dnsmessage.SRVResource{
		Priority: priority,
		Weight:   weight,
		Port:     port,
		Target:   targetName,
	})
	if err != nil {

	if dst == nil {
		return nil, errors.New("ipvs destination should not be empty")
	}
	return &RealServer{
		Address:      dst.Address,
		Port:         dst.Port,
		Weight:       dst.Weight,
		ActiveConn:   dst.ActiveConnections,
		InactiveConn: dst.InactiveConnections,
	}, nil
}

// toIPVSService converts a VirtualServer to the equivalent IPVS Service structure.

// TFL fully_connected basically does:
// Weight * Input + bias.
// Input layout is : [..., depth]
// Weight layout is : [output, depth]
// Bias is [output].
//
// While conv2d is:
// Filter: [NHWC]
// Input is also: [NHWC]
// Bias is [N]
//
// So to perform the transform, we need to insert a few reshape ops:
//
//  Input   weight   bias
//   \      /      /
//       FC
//       |