}

  LiftQuantizableSpotsAsFunctionsDRQPass(
      const LiftQuantizableSpotsAsFunctionsDRQPass& other) {
    quantization_method_ = other.quantization_method_;
    target_opset_ = other.target_opset_;
    min_num_elements_for_weights_ = other.min_num_elements_for_weights_;
  }

  StringRef getArgument() const final {
    // This is the argument used to refer to the pass in

      // Other edges might have `dst` as dst node. Update those edges with
      // `replace_node`.
      for (int j = i + 1, end = data_edges.size(); j < end; j++) {
        if (data_edges[j].dst == dst) {
          data_edges[j].dst = replace_node;
        }
      }

      // Other placeholder node might have `dst` as original node. Update

			switch t := n.Assign.(type) {
			case *ast.ExprStmt:
				assert = t.X.(*ast.TypeAssertExpr)
			case *ast.AssignStmt:
				assert = t.Rhs[0].(*ast.TypeAssertExpr)
			}
			// gather target types from case clauses
			for _, c := range n.Body.List {
				targets = append(targets, c.(*ast.CaseClause).List...)
			}
		}
		V := pass.TypesInfo.TypeOf(assert.X)
		for _, target := range targets {

      func.func @main() -> (tensor<2x3x2x2xf32>) {
        %0 = stablehlo.constant dense<1.0> : tensor<3x4x2xf32>
        %1 = stablehlo.constant dense<2> : tensor<2x3x2xi64>
        %2 = "stablehlo.gather"(%0, %1) {
          dimension_numbers = #stablehlo.gather<
            offset_dims = [2, 3],
            collapsed_slice_dims = [0],
            start_index_map = [1, 0],
            index_vector_dim = 2>,

// CHECK-NEXT:      %17 = "tfl.gather"(%14, %16) <{axis = 0 : i32, batch_dims = 0 : i32}> : (tensor<3xi32>, tensor<2xi64>) -> tensor<2xi32>
// CHECK-NEXT:      %18 = "tfl.pseudo_const"() <{value = dense<1> : tensor<1xi64>}> : () -> tensor<1xi64>
// CHECK-NEXT:      %19 = "tfl.gather"(%15, %18) <{axis = 0 : i32, batch_dims = 0 : i32}> : (tensor<3xi32>, tensor<1xi64>) -> tensor<1xi32>

      : test_mode_(false), calib_opts_(calib_opts) {}

  InsertCustomAggregationOpsPass(const InsertCustomAggregationOpsPass &other) {
    test_mode_ = other.test_mode_;
    test_case_ = other.test_case_;
    calib_opts_ = other.calib_opts_;
    initializeForTest();
  }

  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(InsertCustomAggregationOpsPass)

// Configure `QuantizationSpecs` to apply `StaticRangePtq` to compute heavy
// units.
constexpr absl::string_view kSpecsStaticRangePtqToComputeHeavy =
    R"pb(specs
         [ {
           matcher { function_name { regex: "^.*(conv|dot|gather).*" } }
           method { static_range_ptq {} }
         }])pb";

class TestLiftQuantizableSpotsAsFunctionsWithQuantizationSpecsPass
    : public impl::

// (only `type` and one other matching field should be set at once).
message MetricSpec {
  // type is the type of metric source.  It should be one of "ContainerResource",
  // "External", "Object", "Pods" or "Resource", each mapping to a matching field in the object.
  // Note: "ContainerResource" type is available on when the feature-gate
  // HPAContainerMetrics is enabled
  optional string type = 1;

If you suspect issues with your plugin code, try creating a link:https://scans.gradle.com/[Build Scan] to identify bottlenecks.

}

func addStore(sa *local.IstiodAnalyzer, clusterName string, yamls []string) error {
	client := &fakeClientImpl{
		CLIClient: kube.NewFakeClient(),
		clusterID: cluster.ID(clusterName),
	}
	// Gather test files
	src := file.NewKubeSource(collections.All)
	for i, yamlFile := range yamls {
		data, err := os.ReadFile(yamlFile)
		if err != nil {
			return err
		}