@app.get("/items/")
async def read_items(filter_query: Annotated[FilterParams, Query()]):
    return filter_query
```

Read the new docs: [Query Parameter Models](https://fastapi.tiangolo.com/tutorial/query-param-models/).

#### `Header` Parameter Models

Use Pydantic models for `Header` parameters:

```python
from typing import Annotated

from fastapi import FastAPI, Header
from pydantic import BaseModel

import org.apache.maven.api.annotations.Nonnull;
import org.apache.maven.api.toolchain.ToolchainModel;

/**
 * Factory interface for creating toolchain instances from configuration models.
 *
 * <p>This factory is responsible for instantiating concrete toolchain implementations
 * based on toolchain model configurations or default settings.</p>
 *
 * @since 4.0.0
 */
@Experimental
@Consumer

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

/// info | Información

Para una explicación más completa de `**user_dict` revisa en [la documentación para **Extra Models**](../extra-models.md#about-user_indict){.internal-link target=_blank}.

///

## Devolver el token

El response del endpoint `token` debe ser un objeto JSON.

# Declare Request Example Data { #declare-request-example-data }

You can declare examples of the data your app can receive.

Here are several ways to do it.

## Extra JSON Schema data in Pydantic models { #extra-json-schema-data-in-pydantic-models }

You can declare `examples` for a Pydantic model that will be added to the generated JSON Schema.

//// tab | Pydantic v2

{* ../../docs_src/schema_extra_example/tutorial001_py310.py hl[13:24] *}

/**
 * Represents the source of a model input, such as a POM file.
 * <p>
 * This class tracks the origin of model elements, including their location in source files
 * and relationships between imported models. It's used for error reporting and debugging
 * to help identify where specific model elements came from.
 *
 * @since 4.0.0
 */
public class InputSource implements Serializable {

    private final String modelId;

You probably have seen how to declare the `response_model` and `status_code` for a *path operation*.

That defines the metadata about the main response of a *path operation*.

You can also declare additional responses with their models, status codes, etc.

There's a whole chapter here in the documentation about it, you can read it at [Additional Responses in OpenAPI](additional-responses.md){.internal-link target=_blank}.

## OpenAPI Extra { #openapi-extra }

        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>1.0.0</version>
          <models>
            <model>src/main/mdo/profiles.mdo</model>
            <model>src/main/mdo/paramdoc.mdo</model>
          </models>
        </configuration>
        <executions>
          <execution>
            <id>modello</id>
            <goals>

Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with `Field()`.

//// tab | Pydantic v2

## Resources

*   [TensorFlow.org](https://www.tensorflow.org)
*   [TensorFlow Tutorials](https://www.tensorflow.org/tutorials/)
*   [TensorFlow Official Models](https://github.com/tensorflow/models/tree/master/official)
*   [TensorFlow Examples](https://github.com/tensorflow/examples)
*   [TensorFlow Codelabs](https://codelabs.developers.google.com/?cat=TensorFlow)

	// apply scopes to migrator
	for len(tx.Statement.scopes) > 0 {
		tx = tx.executeScopes()
	}

	return tx.Dialector.Migrator(tx.Session(&Session{}))
}

// AutoMigrate run auto migration for given models
func (db *DB) AutoMigrate(dst ...interface{}) error {
	return db.Migrator().AutoMigrate(dst...)
}

// ViewOption view option
type ViewOption struct {