hashed_password = user_dict["hashed_password"],
)
```

/// info | Дополнительная информация
Более полное объяснение `**user_dict` можно найти в [документации к **Дополнительным моделям**](../extra-models.md#about-user-in-dict){.internal-link target=_blank}.
///

## Возврат токена { #return-the-token }

Ответ операции пути `/token` должен быть объектом JSON.

├── request/
│   ├── suggest/               # Suggestion queries
│   └── popularwords/          # Popular word queries
├── settings/                   # Configuration management
├── entity/                     # Domain models (SuggestItem, etc.)
├── normalizer/                 # Text normalizers
├── converter/                  # Reading converters (katakana, romaji)
├── concurrent/                 # Async patterns (Deferred/Promise)

/// info

Note that the `TestClient` receives data that can be converted to JSON, not Pydantic models.

If you have a Pydantic model in your test and you want to send its data to the application during testing, you can use the `jsonable_encoder` described in [JSON Compatible Encoder](encoder.md){.internal-link target=_blank}.

///

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

        model = builder.build();
        String modelVersion = new MavenModelVersion().getModelVersion(model);
        if (!ModelBuilder.MODEL_VERSION_4_0_0.equals(modelVersion) && !preserveModelVersion) {
            warnNotDowngraded(project);
        }
        model = model.withModelVersion(modelVersion);
        return model;
    }

from sqlmodel.main import default_registry

from tests.utils import needs_py310


def clear_sqlmodel():
    # Clear the tables in the metadata for the default base model
    SQLModel.metadata.clear()
    # Clear the Models associated with the registry, to avoid warnings
    default_registry.dispose()


@pytest.fixture(
    name="client",
    params=[
        pytest.param("tutorial002_py39"),

```

### `RequestValidationError` vs `ValidationError`

/// warning | 警告

如果您觉得现在还用不到以下技术细节，可以先跳过下面的内容。

///

`RequestValidationError` 是 Pydantic 的 <a href="https://docs.pydantic.dev/latest/concepts/models/#error-handling" class="external-link" target="_blank">`ValidationError`</a> 的子类。

**FastAPI** 调用的就是 `RequestValidationError` 类，因此，如果在 `response_model` 中使用 Pydantic 模型，且数据有错误时，在日志中就会看到这个错误。

        </parameter>
      </parameters>

- we need to know what came from the POM, and validate those
- plugin in any default values
- check to see if anything is missing

### Memória por Processo { #memory-per-process }

Agora, quando o programa carrega coisas na memória, por exemplo, um modelo de aprendizado de máquina em uma variável, ou o conteúdo de um arquivo grande em uma variável, tudo isso **consome um pouco da memória (RAM)** do servidor.

from collections.abc import Sequence
from dataclasses import dataclass
from enum import Enum
from typing import Annotated, Any, Callable, Optional, Union

from fastapi.exceptions import FastAPIDeprecationWarning
from fastapi.openapi.models import Example
from pydantic import AliasChoices, AliasPath
from pydantic.fields import FieldInfo
from typing_extensions import Literal, deprecated

from ._compat import (
    Undefined,
)