// `shape_inference::InferenceContext` constructor. Note the following:
//   - The inputs of the `op` are not used for shape inference. So, it is
//     OK to not have the inputs properly set in `op`. See `input_tensors`
//     if you want shape inference to consider the input tensors of the
//     op for shape inference.
//   - The types need not be set in `input_shapes` as it is not used.

    // 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}

    assertThat(graph).isNotEqualTo(g2);
  }

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

    MutableGraph<Integer> g2 =
        GraphBuilder.from(graph).allowsSelfLoops(!graph.allowsSelfLoops()).build();

    assertThat(graph).isNotEqualTo(g2);
  }

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

    MutableGraph<Integer> g2 =
        GraphBuilder.from(graph).allowsSelfLoops(!graph.allowsSelfLoops()).build();

 */
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...

```
(You can paste links or attach files by dragging & dropping them below)
- Provide links to your updated versions of the above two colab notebooks.

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).

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).

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);