once you get into the running container so `bazel` can find the `tensorflow`
    workspace).

    you can do this by using the following command. As an example-

    ```bash
    docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/build:2.15-python3.10
    ```

    Once you have the packages installed, you can run a specific unit test in
    bazel by doing as follows:

    ```bash

# anything with a Windows-only toolchain, and bazel errors if trying to build
# that directory.
@test "bazel nobuild passes on all of TF except TF Lite and win toolchains" {
    bazel build --experimental_cc_shared_library --nobuild --keep_going -- //tensorflow/... -//tensorflow/lite/... -//tensorflow/tools/toolchains/win/... -//tensorflow/tools/toolchains/win_1803/...
}


teardown_file() {
    bazel shutdown

      k -= 1

    # A low-effort attempt to find the bazel command that triggered the
    # invocation.
    bazel_comm_min_line_i = (previous_end_line if previous_end_line is not None
                             else 0)
    while k > bazel_comm_min_line_i:
      backtrack_line = log_lines[k]
      # Don't attempt to parse multi-line commands broken up by backslashes
      if 'bazel ' in backtrack_line and not backtrack_line.endswith('\\'):

# limitations under the License.
# ==============================================================================
setup_file() {
    bazel version  # Start the bazel server
}

# Do a bazel query specifically for the licenses checker. It searches for
# targets matching the provided query, which start with // or @ but not
# //tensorflow (so it looks for //third_party, //external, etc.), and then

def retrieve_bazel_version():
  """Retrieve installed bazel version (or bazelisk).

  Returns:
    The bazel version detected.
  """
  bazel_executable = shutil.which('bazel')
  if bazel_executable is None:
    bazel_executable = shutil.which('bazelisk')
    if bazel_executable is None:
      print('Cannot find bazel. Please install bazel/bazelisk.')
      sys.exit(1)

  stderr = open(os.devnull, 'wb')

        List<String> trail = new ArrayList<>(1);
        trail.add(result.getId());
        result.setDependencyTrail(trail);

        return result;
    }

    public static void toArtifacts(
            Collection<org.apache.maven.artifact.Artifact> artifacts,
            Collection<? extends DependencyNode> nodes,
            List<String> trail,
            DependencyFilter filter) {

* Tecniche più avanzate (ma ugualmente semplici) per dichiarare **modelli JSON altamente nidificati** (grazie a Pydantic).
* E altre funzionalità (grazie a Starlette) come:
    * **WebSockets**
    * **GraphQL**
    * test molto facili basati su `requests` e `pytest`
    * **CORS**
    * **Cookie Sessions**
    * ...e altro ancora.

Bu şema tanımı, API yollarınızla birlikte yollarınızın aldığı olası parametreler gibi tanımlamaları içerir.

#### Veri "Şeması"

"Şema" terimi, JSON içeriği gibi bazı verilerin şeklini de ifade edebilir.

Bu durumda, JSON özellikleri ve sahip oldukları veri türleri gibi anlamlarına gelir.

#### OpenAPI ve JSON Şema

	d.maxSleep = maxWait
	d.cycle = make(chan struct{})
	return nil
}

const (
	// ILMExpiry - audit trail for ILM expiry
	ILMExpiry = "ilm:expiry"
	// ILMFreeVersionDelete - audit trail for ILM free-version delete
	ILMFreeVersionDelete = "ilm:free-version-delete"
	// ILMTransition - audit trail for ILM transitioning.
	ILMTransition = " ilm:transition"
)

Sağladığı basitlik ve esneklik NoSQL veritabanlarını ana veritabanı sistemi olarak kullanmak gibi şeyler yapmaya olanak sağlar.

Yapısı oldukça basit olduğundan öğrenmesi de nispeten basittir, tabii dökümantasyonu bazı noktalarda biraz teknik hale geliyor.