{Name: names.VolumeZone},
									{Name: names.PodTopologySpread, Weight: ptr.To[int32](2)},
									{Name: names.InterPodAffinity, Weight: ptr.To[int32](2)},
									{Name: names.DefaultPreemption},
									{Name: names.NodeResourcesBalancedAllocation, Weight: ptr.To[int32](1)},
									{Name: names.ImageLocality, Weight: ptr.To[int32](1)},
									{Name: names.DefaultBinder},
								},
							},
							Bind: configv1.PluginSet{

.docs-navigation > ul ul ul {
    display: none;
    height: 0;
    margin-left: 1rem;
}

.docs-navigation > ul ul:target,
.docs-navigation > ul ul:target ul,
.docs-navigation > ul .nav-dropdown.expanded ~ ul {
    display: block;
    height: auto;
}

.docs-navigation h3 {
    font-size: .95rem;
    font-weight: 600;
    line-height: 1.5;
    margin: 1.5em 0 0;
}

  %7 = "quantfork.dcast"(%6) : (tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>) -> tensor<*xf32>
  %weight = arith.constant dense<1.0> : tensor<144x12xf32>
  %q_weight = "quantfork.qcast"(%weight) : (tensor<144x12xf32>) -> tensor<144x12x!quant.uniform<i8:f32, 0.074855112561992565:-1>>

		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()

// 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
//       |

//===----------------------------------------------------------------------===//

// CHECK-LABEL: func @quantized_matmul_fn
func.func @quantized_matmul_fn(%input: tensor<?xf32>) -> tensor<?xf32> {

  func.return %fc : tensor<1x2xf32>
// CHECK-DAG: %[[weight:.*]] = arith.constant dense<{{\[\[}}0.000000e+00, 1.000000e+00]
// CHECK-DAG: %[[bias:.*]] = arith.constant dense<[0.000000e+00, 2147364.75]>
// CHECK-DAG: %[[b_q:.*]] = "tfl.quantize"(%[[bias]]){{.*}}quant.uniform<i32:f32:0, {7.8740158861230386E-10,0.0019998892694710656}>>

	extenderManagedResources := sets.New[string]()
	for i, extender := range extenders {
		path := fldPath.Index(i)
		if len(extender.PrioritizeVerb) > 0 && extender.Weight <= 0 {
			errs = append(errs, field.Invalid(path.Child("weight"),
				extender.Weight, "must have a positive weight applied to it"))
		}
		if extender.BindVerb != "" {
			binders++
		}
		for j, resource := range extender.ManagedResources {

  return ConstantFoldOpIfPossible(reshape_op).front();
}

// Checks if a value can be symmetrically quantized.
bool CanBeSymmetricallyQuantized(Value weight) {
  auto dq_op = weight.getDefiningOp<quantfork::DequantizeCastOp>();
  if (!dq_op) return true;

  auto qtype =
      mlir::cast<TensorType>(dq_op.getArg().getType()).getElementType();

	weighted := make([]*route.WeightedCluster_ClusterWeight, 0)
	for _, dst := range in.Route {
		weight := &wrappers.UInt32Value{Value: uint32(dst.Weight)}
		if dst.Weight == 0 {
			// Ignore 0 weighted clusters if there are other clusters in the route.
			// But if this is the only cluster in the route, then add it as a cluster with weight 100
			if len(in.Route) == 1 {