suite("Gradle Test Executor") {
                        testClass("org.example.ComplexTests") {
                            operationDisplayName "some_name for_tests"
                            testDisplayName "some_name for_tests"

                            test("test()") {
                                operationDisplayName "test"
                                testDisplayName "test"

        String someName = "someName"
        String someVersion = "someVersion"
        String someConfiguration = "someConfiguration"
        DefaultResolvedDependency resolvedDependency = new DefaultResolvedDependency(someConfiguration, newId(someGroup, someName, someVersion), buildOperationProcessor)

        then:
        resolvedDependency.name == someGroup + ":" + someName + ":" + someVersion

// IntPerLayer-DAG: "tf.DumpTensor"(%[[output1_unquantized]]) <{enabled = true, file_name = "unquantized_tensor_data.pb", func_name = "conv", log_dir_path = "/tmp/dumps/composite_conv2d_with_bias_and_relu6_fn_1", node_name = "Conv2D_1"}> : (tensor<*xf32>) -> ()
// IntPerLayer-DAG: return %[[output0]], %[[output1_quantized]]

    string file_name;
    string func_name;
    string node_name;
    OP_REQUIRES_OK(ctx, ctx->GetAttr("log_dir_path", &log_dir_path));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("enabled", &enabled_));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("file_name", &file_name));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("func_name", &func_name));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("node_name", &node_name));

    if log-dump-ssh "${node_name}" "command -v journalctl" &> /dev/null; then
        if [[ "${on_master}" == "true" ]]; then
          log-dump-ssh "${node_name}" "sudo journalctl --output=short-precise -u kube-master-installation.service" > "${dir}/kube-master-installation.log" || true

// WholeModel-DAG: %[[matmul1_q:.*]] = "tf.XlaCallModule"(%[[matmul0_q]], %[[w0]], %[[b0]]) <{Sout = [#tf_type.shape<?x2>], module = "", version = 9 : i64}> {_entry_function = @composite_dot_genera...

module attributes {tf_saved_model.semantics} {

  // Test case: Fail if some global tensor ops remain

 "tf_saved_model.global_tensor"() {sym_name = "v", type = tensor<f32>, value = dense<1.0> : tensor<f32> } : () -> ()
 "tf_saved_model.global_tensor"() {sym_name = "v2", type = tensor<f32>, value = dense<1.0> : tensor<f32> } : () -> ()

  func.func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @v})

    const FlatSymbolRefAttr f_attr = GetFuncAttr(op);

    // In TF::PartitionedCallOp case, func_name and node_name are filled.
    // But in TF::XlaCallModuleOp case, node_name is `kEmptyNodeName` since
    // debugging and selective quantization of StableHLO Quantizer only uses
    // func_name for op matching.
    auto [func_name, node_name] = GetFuncNameAndNodeName(op, f_attr);
    std::string folder_name =

    "func.return"() : () -> ()
  }) {sym_name = "__inference_wrapped_function_4260250_0", sym_visibility = "private", tf._input_shapes = [#tf_type.shape<>], tf.signature.is_stateful, function_type = (tensor<i32>) -> ()} : () -> ()
  "func.func"() ({
  ^bb0(%arg0: tensor<i32>):
    "func.return"(%arg0) : (tensor<i32>) -> ()

          custom_op_map[node_name].quantizable_input_indices.push_back(
              std::stoi(cur_index));
        }
        break;
      }
      case CustomOpUpdateOptions::kWeightOnly:
        custom_op_map[node_name].is_weight_only =
            GetBooleanSpecs(node_specification);
        break;
      case CustomOpUpdateOptions::kNoSideEffect:
        custom_op_map[node_name].no_side_effect =