Disabled []Plugin
}

// Plugin specifies a plugin name and its weight when applicable. Weight is used only for Score plugins.
type Plugin struct {
	// Name defines the name of plugin
	Name string
	// Weight defines the weight of plugin, only used for Score plugins.
	Weight int32
}

// PluginConfig specifies arguments that should be passed to a plugin at the time of initialization.

// however we don't care about topology weight in this case as we return a 0
// score for all nodes.
func topologyNormalizingWeight(size int) float64 {
	return math.Log(float64(size + 2))
}

// scoreForCount calculates the score based on number of matching pods in a
// topology domain, the constraint's maxSkew and the topology weight.
// `maxSkew-1` is added to the score so that differences between topology

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

// 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 {

    %weight = "tf.Const"() {value = dense<2.000000e+00> : tensor<2x3x3x2xf32>} : () -> tensor<2x3x3x2xf32>
    %1 = "tf.PartitionedCall"(%arg0, %weight) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_conv2d_fn_1} : (tensor<1x2x2x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>

	out.URLPrefix = in.URLPrefix
	out.FilterVerb = in.FilterVerb
	out.PreemptVerb = in.PreemptVerb
	out.PrioritizeVerb = in.PrioritizeVerb
	out.Weight = in.Weight
	out.BindVerb = in.BindVerb
	out.EnableHTTPS = in.EnableHTTPS
	out.TLSConfig = (*config.ExtenderTLSConfig)(unsafe.Pointer(in.TLSConfig))
	out.HTTPTimeout = in.HTTPTimeout
	out.NodeCacheCapable = in.NodeCacheCapable

		extenderURL:      config.URLPrefix,
		preemptVerb:      config.PreemptVerb,
		filterVerb:       config.FilterVerb,
		prioritizeVerb:   config.PrioritizeVerb,
		bindVerb:         config.BindVerb,
		weight:           config.Weight,
		client:           client,
		nodeCacheCapable: config.NodeCacheCapable,
		managedResources: managedResources,
		ignorable:        config.Ignorable,
	}, nil
}

    %weight = "tf.Const"() {value = dense<2.000000e+00> : tensor<2x3x3x2xf32>} : () -> tensor<2x3x3x2xf32>
    %1 = "tf.PartitionedCall"(%arg0, %weight) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_conv2d_fn_1} : (tensor<1x2x2x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>

  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();