## <a href="https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker" class="external-link" target="_blank">tiangolo/uvicorn-gunicorn-fastapi</a>

Cette image est dotée d'un mécanisme d'"auto-tuning", de sorte qu'il vous suffit d'ajouter votre code pour obtenir automatiquement des performances très élevées. Et sans faire de sacrifices.

    numerical_grad.reset(numerical_grad_raw);
  }

  TF_Tensor* numerical_tensor;
  s = GetValue(numerical_grad.get(), &numerical_tensor);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto num_elem_numerical = TF_TensorElementCount(numerical_tensor);
  ASSERT_EQ(num_elem_numerical, num_grad);

  float* dnumerical = new float[num_elem_numerical]{0};
  memcpy(&dnumerical[0], TF_TensorData(numerical_tensor),

Então eu contribuí com ele, para deixá-lo completamente de acordo com o JSON Schema, para dar suporte a diferentes maneiras de definir declarações de restrições, e melhorar o suporte a editores (conferências de tipos, auto completações) baseado nos testes em vários editores.

Durante o desenvolvimento, eu também contribuí com o <a href="https://www.starlette.io/" class="external-link" target="_blank">**Starlette**</a>, outro requisito chave.

    dep ->> operation: Run dependency, e.g. DB session
    opt raise
        operation -->> dep: Raise HTTPException
        dep -->> handler: Auto forward exception
        handler -->> client: HTTP error response
        operation -->> dep: Raise other exception
        dep -->> handler: Auto forward exception
    end
    operation ->> client: Return response to client

\"field\":\"responseTime\"}},{\"id\":\"2\",\"enabled\":true,\"type\":\"date_histogram\",\"schema\":\"segment\",\"params\":{\"field\":\"requestedAt\",\"timeRange\":{\"from\":\"now-2d\",\"to\":\"now\"},\"useNormalizedEsInterval\":true,\"interval\":\"auto\",\"drop_partials\":false,\"min_doc_count\":1,\"extended_bounds\":{}}}]}"},"id":"3826fb80-b2be-11e9-b23e-033a71496733","migrationVersion":{"visualization":"7.2.0"},"references":[{"id":"17d7d930-b2b4-11e9-b23e-033a71496733","name":"kibanaSavedObjec...

* trong <a href="https://www.jetbrains.com/pycharm/" class="external-link" target="_blank">PyCharm</a>:

![editor support](https://fastapi.tiangolo.com/img/pycharm-completion.png)

Bạn sẽ có được auto-completion trong code, thậm chí trước đó là không thể. Như trong ví dụ, khóa `price` bên trong một JSON (đó có thể được lồng nhau) đến từ một request.

  // rejected by a future step in the admission chain and the side effects therefore need to be undone.
  // Requests with the dryRun attribute will be auto-rejected if they match a webhook with
  // sideEffects == Unknown or Some. Defaults to Unknown.
  // +optional
  optional string sideEffects = 6;

	for _, column := range columns {
		if column.Name() == "log_id" {
			hasLogID = true
			autoIncrement, ok := column.AutoIncrement()
			if !ok || !autoIncrement {
				t.Fatalf("column log_id should be auto incrementment")
			}
		}
	}

	if !hasLogID {
		t.Fatalf("failed to found column log_id")
	}
}

type Post struct {

  TFE_TensorHandle* retvals[1];
  int num_retvals = 1;
  TFE_Execute(matmul, &retvals[0], &num_retvals, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  auto* retval_task0 =
      TFE_TensorHandleCopyToDevice(retvals[0], ctx, local_device_name, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  CheckTFE_TensorHandleHasFloats(retval_task0, {7, 10, 15, 22});

    const char* attr_name, absl::Span<string const> values) {
  std::vector<const char*> raw_strs;
  std::vector<size_t> lengths;
  raw_strs.reserve(values.size());
  lengths.reserve(values.size());
  for (const auto& s : values) {
    raw_strs.emplace_back(s.data());
    lengths.emplace_back(s.size());
  }
  return SetAttrStringList(attr_name,
                           reinterpret_cast<const void**>(raw_strs.data()),