// We define the arithmetic means X_n = 1/n \sum_{i=1}^n x_i, and Y_n = 1/n \sum_{i=1}^n y_i.
    // We also define the sum of the products of the differences from the means
    //           C_n = \sum_{i=1}^n x_i y_i - n X_n Y_n
    // for all n >= 1. Then for all n > 1:
    //       C_{n-1} = \sum_{i=1}^{n-1} x_i y_i - (n-1) X_{n-1} Y_{n-1}

 */
class ConscryptPlatform private constructor() : Platform() {
  private val provider: Provider = Conscrypt.newProvider()

  // See release notes https://groups.google.com/forum/#!forum/conscrypt
  // for version differences
  override fun newSSLContext(): SSLContext =
    // supports TLSv1.3 by default (version api is >= 1.4.0)
    SSLContext.getInstance("TLS", provider)

  override fun platformTrustManager(): X509TrustManager {

“parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and...

Ainsi, avant même de commencer à coder **FastAPI**, j'ai passé plusieurs mois à étudier les spécifications d'OpenAPI, JSON Schema, OAuth2, etc. Comprendre leurs relations, leurs similarités et leurs différences.

## Conception

Ensuite, j'ai passé du temps à concevoir l'"API" de développeur que je voulais avoir en tant qu'utilisateur (en tant que développeur utilisant FastAPI).

   * 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() {

Also, the best approach was to use already existing standards.

So, before even starting to code **FastAPI**, I spent several months studying the specs for OpenAPI, JSON Schema, OAuth2, etc. Understanding their relationship, overlap, and differences.

## Design { #design }

Then I spent some time designing the developer "API" I wanted to have as a user (as a developer using FastAPI).

    assertThat(network).isNotEqualTo(g2);
  }

  // Node/edge sets and node/edge connections are the same, but network properties differ.
  // (In this case the networks are considered equivalent; the property differences are irrelevant.)
  @Test
  public void equivalent_propertiesDiffer() {
    network.addEdge(N1, N2, E12);

    MutableNetwork<Integer, String> g2 =
        NetworkBuilder.from(network)

    assertThat(network).isNotEqualTo(g2);
  }

  // Node/edge sets and node/edge connections are the same, but network properties differ.
  // (In this case the networks are considered equivalent; the property differences are irrelevant.)
  @Test
  public void equivalent_propertiesDiffer() {
    network.addEdge(N1, N2, E12);

    MutableNetwork<Integer, String> g2 =
        NetworkBuilder.from(network)

                    .get());
            options.addOption(Option.builder(INFER)
                    .longOpt("infer")
                    .desc("Use inference when upgrading (remove redundant information)")
                    .get());
            options.addOption(Option.builder(MODEL)
                    .longOpt("model")

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);