*           .thenComparing(foo -> foo.anEnum, nullsLast(naturalOrder()));
 *
 *   @Override
 *   public int compareTo(Foo that) {
 *     return COMPARATOR.compare(this, that);
 *   }
 * }
 *
 * <p>With method references it is more succinct: {@code comparing(Foo::aString)} for example.
 *
 * <p>Using {@link Comparator} avoids certain types of bugs, for example when you meant to write

  }

  /**
   * Returns a new {@code MutableClassToInstanceMap} instance backed by a given empty {@code
   * backingMap}. The caller surrenders control of the backing map, and thus should not allow any
   * direct references to it to remain accessible.
   */
  public static <B extends @Nullable Object> MutableClassToInstanceMap<B> create(
      Map<Class<? extends @NonNull B>, B> backingMap) {
    return new MutableClassToInstanceMap<>(backingMap);

   * use the {@code ArrayList} {@linkplain ArrayList#ArrayList() constructor} directly, taking
   * advantage of <a
   * href="https://docs.oracle.com/javase/tutorial/java/generics/genTypeInference.html#type-inference-instantiation">"diamond"
   * syntax</a>.
   */
  @SuppressWarnings("NonApiType") // acts as a direct substitute for a constructor call
  public static <E extends @Nullable Object> ArrayList<E> newArrayList() {

Ini juga dibangun untuk digunakan sebagai referensi yang akan datang.

Sehingga kamu dapat kembali lagi dan mencari apa yang kamu butuhkan dengan tepat.

## Jalankan kode

struct TF_Library {
  void* lib_handle;
  TF_Buffer op_list;
};

struct TF_Graph {
  TF_Graph();

  mutable tensorflow::mutex mu;
  tensorflow::Graph graph TF_GUARDED_BY(mu);

  // Runs shape inference.
  tensorflow::ShapeRefiner refiner TF_GUARDED_BY(mu);

  // Maps from name of an operation to the Node* in 'graph'.
  std::unordered_map<tensorflow::string, tensorflow::Node*> name_map
      TF_GUARDED_BY(mu);

    /**
     * Append formatted content to the buffer.
     * @see String#format(String, Object...)
     *
     * @param pattern a <a href="../util/Formatter.html#syntax">format string</a>
     * @param args arguments referenced by the format specifiers in the format string
     * @return the current builder
     */
    @Nonnull
    default MessageBuilder format(String pattern, Object... args) {

      status->status = TF_TensorToTensor(input_tensors[i], &input_tensor);
      if (!status->status.ok()) return;
      input_tensors_vector[i] = &input_tensor;
    }
  }

  // Create an inference context with dummy values, which will be updated later.
  InferenceContext c(TF_GRAPH_DEF_VERSION, node_def, op_reg_data->op_def,
                     std::vector<ShapeHandle>(num_inputs), input_tensors_vector,

Cada seção constrói, gradualmente, sobre as anteriores, mas sua estrutura são tópicos separados, para que você possa ir a qualquer um específico e resolver suas necessidades específicas de API.

Ele também foi construído para servir como uma referência futura, então você pode voltar e ver exatamente o que você precisa.

## Rode o código

Todos os blocos de código podem ser copiados e utilizados diretamente (eles são, na verdade, arquivos Python testados).

            if (verbose) {
              println(record.message)
              println(record.parameters[0])
            }

            // JSSE logs additional messages as parameters that are not referenced in the log message.
            val parameter = parameters[0] as String

            if (message == "Produced ClientHello handshake message") {
              random = readClientRandom(parameter)
            }

   * <i>happen-before</i></a> its execution begins, perhaps in another thread.
   *
   * <p>Guava implementations of {@code ListenableFuture} promptly release references to listeners
   * after executing them.
   *
   * @param listener the listener to run when the computation is complete
   * @param executor the executor to run the listener in