As FastAPI will not try to convert the data to JSON with Pydantic or serialize it in any way, in this case, the type annotation is only for your editor and tools to use, it won't be used by FastAPI.

{* ../../docs_src/stream_data/tutorial001_py310.py ln[32:35] hl[33] *}

## Decoding Metadata

Metadata is stored in `xl.meta` files for erasure coded objects. Each disk in the set containing the object has this file. The file format is a binary format and therefore requires tools to view values.

### Installing xl-meta

To install, [Go](https://golang.org/dl/) must be installed. Once installed, execute this to install the binary:

```bash

### Pydantic v1 parameters { #pydantic-v1-parameters }

If you need to use some of the FastAPI-specific tools for parameters like `Body`, `Query`, `Form`, etc. with Pydantic v1 models, you can import them from `fastapi.temp_pydantic_v1_params` while you finish the migration to Pydantic v2:

import java.util.function.UnaryOperator;

import org.apache.maven.api.annotations.Experimental;
import org.apache.maven.api.annotations.Nonnull;

/**
 * Represents the base options supported by Maven tools.
 * This interface defines methods to access various configuration options
 * that can be set through command-line arguments or configuration files.
 *
 * @since 4.0.0
 */
@Experimental
public interface Options {

 * variables. An {@code AtomicDouble} is used in applications such as atomic accumulation, and
 * cannot be used as a replacement for a {@link Double}. However, this class does extend {@code
 * Number} to allow uniform access by tools and utilities that deal with numerically-based classes.
 *
 * <p><a id="bitEquals"></a>This class compares primitive {@code double} values in methods such as

Adding an `\f` (an escaped "form feed" character) causes **FastAPI** to truncate the output used for OpenAPI at this point.

It won't show up in the documentation, but other tools (such as Sphinx) will be able to use the rest.

{* ../../docs_src/path_operation_advanced_configuration/tutorial004_py310.py hl[17:27] *}

## Additional Responses { #additional-responses }

然后，我又花了一些时间从用户角度（使用 FastAPI 的开发者）设计了开发者 **API**。

同时，我还在最流行的 Python 代码编辑器中测试了很多思路，包括 PyCharm、VS Code、基于 Jedi 的编辑器。

根据最新 [Python 开发者调研报告](https://www.jetbrains.com/research/python-developers-survey-2018/#development-tools)显示，这几种编辑器覆盖了约 80% 的用户。

也就是说，**FastAPI** 针对差不多 80% 的 Python 开发者使用的编辑器进行了测试，而且其它大多数编辑器的工作方式也与之类似，因此，**FastAPI** 的优势几乎能在所有编辑器上体现。

通过这种方式，我就能找到尽可能减少代码重复的最佳方式，进而实现处处都有自动补全、类型提示与错误检查等支持。

所有这些都是为了给开发者提供最佳的开发体验。

  pip install twine==3.6.0
fi

# When cross-compiling with RBE, we need to copy the macOS sysroot to be
# inside the TensorFlow root directory. We then define them as a filegroup
# target inside "tensorflow/tools/toolchains/cross_compile/cc" so that Bazel
# can register it as an input to compile/link actions and send it to the remote
# VMs when needed.
# TODO(b/316932689): Avoid copying and replace with a local repository rule.

Later, for your production application, you might want to use a database server like **PostgreSQL**.

/// tip

There is an official project generator with **FastAPI** and **PostgreSQL** including a frontend and more tools: [https://github.com/fastapi/full-stack-fastapi-template](https://github.com/fastapi/full-stack-fastapi-template)

///

 [okio_3_6_0]: https://square.github.io/okio/changelog/#version-360
 [public_suffix]: https://publicsuffix.org/
 [rfc_2045]: https://tools.ietf.org/html/rfc2045
 [rfc_7231_647]: https://tools.ietf.org/html/rfc7231#section-6.4.7
 [rfc_7692]: https://tools.ietf.org/html/rfc7692
 [semver]: https://semver.org/