%scale = "tf.Const"() {value = dense<2.0> : tensor<f32>} : () -> tensor<f32>
        %weight = "tf.Const"() {value = dense<1> : tensor<1024x24x24x3xi8>} : () -> tensor<1024x24x24x3xi8>
        %input_i32 = "tf.Cast"(%weight) : (tensor<1024x24x24x3xi8>) -> tensor<1024x24x24x3xi32>
        %output = "tf.Sub"(%input_i32, %zp) : (tensor<1024x24x24x3xi32>, tensor<i32>) -> tensor<1024x24x24x3xi32>

//       value {dimension_specs {dimension: 3}}}
//   }}
// }
// ```
//
// This preset:
//   * Applies per-channel quantization for weights (input index 1) of
//     convolution quantizable unit family. The quantization dimension is 3, the
//     channel dimension, which assumes the weight tensor is in NHWC format.
//   * Applies static-range PTQ for all other ops.
//
// Next ID: 4
message StaticRangePtqPreset {

					Prioritizers: []tf.PriorityConfig{{Function: tf.Node1PrioritizerExtender, Weight: 10}},
					Weight:       1,
				},
				{
					ExtenderName: "FakeExtender2",
					Predicates:   []tf.FitPredicate{tf.TruePredicateExtender},
					Prioritizers: []tf.PriorityConfig{{Function: tf.Node2PrioritizerExtender, Weight: 10}},
					Weight:       5,
				},
			},
			nodes: []string{"node1", "node2"},

	for _, prioritizer := range f.Prioritizers {
		weight := prioritizer.Weight
		if weight == 0 {
			continue
		}
		priorityFunc := prioritizer.Function
		prioritizedList, err := priorityFunc(pod, nodes)
		if err != nil {
			return &extenderv1.HostPriorityList{}, 0, err
		}
		for _, hostEntry := range *prioritizedList {
			combinedScores[hostEntry.Name] += hostEntry.Score * weight
		}
	}

// 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> {

	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();

  // quantizable ops, Q-DQ ops need to be preserved.
  bool shouldKeepUnusedQdqPattern();

  void runOnOperation() override;

 private:
  QuantizationSpecs quant_specs_;

  Option<bool> weight_quantization_{
      *this, "weight-quantization", llvm::cl::init(false),
      llvm::cl::desc("Whether to enable weight quantization.")};
  Option<OpSet> target_opset_{

  // prepare_quantize_ptq_per_channel.mlir.
  Option<bool> enable_per_channel_quantization_{
      *this, "enable-per-channel-quantization", llvm::cl::init(false),
      llvm::cl::desc("Whether enable per-channel quantized weights.")};
};

bool PrepareQuantizePass::SetInputNodesQuantizationParams(func::FuncOp func) {
  StringRef func_name = func.getName();
  auto has_quantize_op = [&](const Value arg) {
    return (arg.hasOneUse() &&