/*
 * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
 * or more contributor license agreements. Licensed under the Elastic License
 * 2.0 and the Server Side Public License, v 1; you may not use this file except
 * in compliance with, at your election, the Elastic License 2.0 or the Server
 * Side Public License, v 1.
 */

package org.elasticsearch.gradle.internal.conventions;

import java.util.Locale;

/*
 * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
 * or more contributor license agreements. Licensed under the Elastic License
 * 2.0 and the Server Side Public License, v 1; you may not use this file except
 * in compliance with, at your election, the Elastic License 2.0 or the Server
 * Side Public License, v 1.
 */

package org.elasticsearch.gradle.internal.distribution;

/*
 * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
 * or more contributor license agreements. Licensed under the Elastic License
 * 2.0 and the Server Side Public License, v 1; you may not use this file except
 * in compliance with, at your election, the Elastic License 2.0 or the Server
 * Side Public License, v 1.
 */

package org.elasticsearch.gradle.internal.distribution;

/*
 * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
 * or more contributor license agreements. Licensed under the Elastic License
 * 2.0 and the Server Side Public License, v 1; you may not use this file except
 * in compliance with, at your election, the Elastic License 2.0 or the Server
 * Side Public License, v 1.
 */

package org.elasticsearch.gradle.internal.distribution;

# Resources { #resources }

hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

Here are several pointers to other places in the docs, for general or frequent questions.

## Filter Data - Security { #filter-data-security }

To ensure that you don't return more data than you should, read the docs for [Tutorial - Response Model - Return Type](../tutorial/response-model.md).

## Optimize Response Performance - Response Model - Return Type { #optimize-response-performance-response-model-return-type }

# About { #about }

When you need `lifespan` to run in your tests, you can use the `TestClient` with a `with` statement:

{* ../../docs_src/app_testing/tutorial004_py310.py hl[9:15,18,27:28,30:32,41:43] *}


You can read more details about the ["Running lifespan in tests in the official Starlette documentation site."](https://www.starlette.dev/lifespan/#running-lifespan-in-tests)

You can also use it directly to create an instance of it and return it from your *path operations*.

Read more about it in the [FastAPI docs about returning a custom Response](https://fastapi.tiangolo.com/advanced/response-directly/#returning-a-custom-response)

You can import it directly from `fastapi`:

```python