tensorflow::shape_inference::InferenceContext* ic, int num_dims,
    const int64_t* dims) {
  if (num_dims != -1) {
    std::vector<tensorflow::shape_inference::DimensionHandle> dim_vec;
    dim_vec.reserve(num_dims);
    for (int i = 0; i < num_dims; ++i) {
      dim_vec.push_back(ic->MakeDim(dims[i]));
    }
    return ic->MakeShape(dim_vec);
  } else {
    return ic->UnknownShape();
  }
}

            "description": "Production environment"
        }
    ],
    "paths": {
            // Más cosas aquí
    }
}
```

/// tip | Consejo

Observa el server auto-generado con un valor `url` de `/api/v1`, tomado del `root_path`.

///

En la UI de los docs en [http://127.0.0.1:9999/api/v1/docs](http://127.0.0.1:9999/api/v1/docs) se vería como:

   * {@code lexicographical(Collections.reverseOrder(comparator))} (consider how each would order
   * {@code [1]} and {@code [1, 1]}).
   */
  // Note: 90% of the time we don't add type parameters or wildcards that serve only to "tweak" the
  // desired return type. However, *nested* generics introduce a special class of problems that we
  // think tip it over into being worthwhile.

scenario would require a malicious user to be able to control the model which as
stated above is already equivalent to code execution. In any case, the
recommendation is not to serve models using Eager mode since it also has
performance limitations.

## Multi-Tenant environments

It is possible to run multiple TensorFlow models in parallel. For example,

  for (int i = 0; i < inputs.size(); i++) {
    input_ids[i] = ToId(inputs[i]);
    input_dtypes[i] = inputs[i]->DataType();
  }
  std::vector<TapeTensor> tape_tensors;
  tape_tensors.reserve(outputs.size());
  for (auto t : outputs) {
    tape_tensors.push_back(TapeTensor(t));
  }
  GradientTape::RecordOperation(
      op_name, tape_tensors, input_ids, input_dtypes,

E esses modelos estão compartilhando muitos dos dados e duplicando nomes e tipos de atributos.

Nós poderíamos fazer melhor.

Podemos declarar um modelo `UserBase` que serve como base para nossos outros modelos. E então podemos fazer subclasses desse modelo que herdam seus atributos (declarações de tipo, validação, etc.).

Toda conversão de dados, validação, documentação, etc. ainda funcionará normalmente.

```
.
├── app
│   ├── __init__.py
│   ├── main.py
└── static
    ├── redoc.standalone.js
    ├── swagger-ui-bundle.js
    └── swagger-ui.css
```

### Statik dosyaları servis edin { #serve-the-static-files }

* `StaticFiles` içe aktarın.
* Belirli bir path'te bir `StaticFiles()` instance'ını "mount" edin.

{* ../../docs_src/custom_docs_ui/tutorial002_py310.py hl[7,11] *}

```

...desde:

```Python
        ... "item_name": item.name ...
```

...a:

```Python
        ... "item_price": item.price ...
```

...y observa cómo tu editor autocompleta los atributos y conoce sus tipos:

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

func (m *Manager) AddToMux(router *mux.Router, authReq func(r *http.Request) error) {
	router.Handle(m.routePath, m.Handler(authReq))
}

// Handler returns a handler that can be used to serve grid requests.
// This should be connected on RoutePath to the main server.
func (m *Manager) Handler(authReq func(r *http.Request) error) http.HandlerFunc {
	return func(w http.ResponseWriter, req *http.Request) {

   * {@code lexicographical(Collections.reverseOrder(comparator))} (consider how each would order
   * {@code [1]} and {@code [1, 1]}).
   */
  // Note: 90% of the time we don't add type parameters or wildcards that serve only to "tweak" the
  // desired return type. However, *nested* generics introduce a special class of problems that we
  // think tip it over into being worthwhile.