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

  // Exercise kernel NodeDef name read
  TF_StringView name_string_view = TF_OpKernelConstruction_GetName(ctx);
  std::string node_name = "SomeNodeName";
  std::string candidate_node_name =
      std::string(name_string_view.data, name_string_view.len);
  EXPECT_EQ(node_name, candidate_node_name);
  return s;
}

static void MyComputeFunc(void* kernel, TF_OpKernelContext* ctx) {

// 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})

// tag::publish-distribution[]
plugins {
// end::publish-distribution[]
    id 'distribution'
}
// end::use-plugin[]

// tag::configure-distribution[]
distributions {
    main {
        distributionBaseName = 'someName'
        distributionClassifier = 'classifier'
        contents {
            from 'src/readme'
        }
    }
}
// end::configure-distribution[]

// tag::custom-distribution[]
distributions {

    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 =

// tag::use-plugin[]
plugins {
    distribution
}
// end::use-plugin[]

// tag::configure-distribution[]
distributions {
    main {
        distributionBaseName = "someName"
        distributionClassifier = "classifier"
        contents {
            from("src/readme")
        }
    }
}
// end::configure-distribution[]

// tag::custom-distribution[]
distributions {
    create("custom") {

    "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 =

constexpr std::string_view kQuantizationUnitSuffix = ")";

// Concatenates node name and func name with a "@" separator.
std::string ConcatNodeAndFuncName(std::string_view node_name,
                                  std::string_view func_name) {
  return absl::StrCat(node_name, "@", func_name);
}

// Generate a string to represent the QuantizationUnit.
std::string GenerateQuantizationUnitString(