*/

package com.google.common.util.concurrent.internal;


/**
 * Static utilities for {@link InternalFutureFailureAccess}. Most users will never need to use this
 * class.
 *
 * <p>This class is GWT-compatible.
 *
 * @since {@code com.google.guava:failureaccess:1.0}, which was added as a dependency of Guava in
 *     Guava 27.0
 */
public final class InternalFutures {
  /**

You can import it directly from `fastapi`:

```python
from fastapi import Request
```

/// tip

When you want to define dependencies that should be compatible with both HTTP and WebSockets, you can define a parameter that takes an `HTTPConnection` instead of a `Request` or a `WebSocket`.

///

In a typical modern infrastructure deployment, application, database, key-store, etc. already live in containers and are managed by orchestration platforms. MinIO brings robust, scalable, AWS S3 compatible object storage to the lot.

   without changing `.kt` files.

With a few small exceptions (below), OkHttp 4.x is both binary- and Java source-compatible with
OkHttp 3.x. You can use an OkHttp 4.x .jar file with applications or libraries built for OkHttp 3.x.

OkHttp is **not** source-compatible for Kotlin callers, but upgrading should be automatic thanks to
Kotlin’s powerful deprecation features. Most developers should be able to use IntelliJ’s _Code

	}

	listObjectVersions := objectAPI.ListObjectVersions

	// Initiate a list object versions operation based on the input params.
	// On success would return back ListObjectsInfo object to be
	// marshaled into S3 compatible XML header.
	listObjectVersionsInfo, err := listObjectVersions(ctx, bucket, prefix, marker, versionIDMarker, delimiter, maxkeys)
	if err != nil {
		writeErrorResponse(ctx, w, toAPIError(ctx, err), r.URL)
		return
	}

Esto te permitiría **reutilizar el modelo** en **múltiples lugares** y también declarar validaciones y metadatos para todos los parámetros de una vez. 😎

/// note | Nota

Esto es compatible desde la versión `0.115.0` de FastAPI. 🤓

///

## Parámetros Query con un Modelo Pydantic

Declara los **parámetros query** que necesitas en un **modelo de Pydantic**, y luego declara el parámetro como `Query`:

        checkpoint with a root object. This allows users to create a
        `Checkpoint` object that is compatible with Keras `model.save_weights()`
        and `model.load_weights`. The checkpoint is also compatible with the
        checkpoint saved in the `variables/` folder in the SavedModel.
    *   When restoring, `save_path` can be a path to a SavedModel. The function

<%@page pageEncoding="UTF-8" contentType="text/html; charset=UTF-8"%>
<meta content='width=device-width, initial-scale=1, maximum-scale=1, user-scalable=no' name='viewport'>
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<link href="${fe:url('/css/admin/bootstrap.min.css')}" rel="stylesheet" type="text/css" />
<link href="${fe:url('/css/admin/font-awesome.min.css')}" rel="stylesheet" type="text/css" />

   * with the directionality of this graph.
   */
  protected final void validateEndpoints(EndpointPair<?> endpoints) {
    checkNotNull(endpoints);
    checkArgument(isOrderingCompatible(endpoints), ENDPOINTS_MISMATCH);
  }

  /**
   * Returns {@code true} iff {@code endpoints}' ordering is compatible with the directionality of
   * this graph.
   */

In all the docs there are examples compatible with each version of Python (when there's a difference).

For example "**Python 3.6+**" means it's compatible with Python 3.6 or above (including 3.7, 3.8, 3.9, 3.10, etc). And "**Python 3.9+**" means it's compatible with Python 3.9 or above (including 3.10, etc).