- name: Setup uv
        uses: astral-sh/setup-uv@v7
        with:
          cache-dependency-glob: |
            pyproject.toml
            uv.lock
      - name: Install Dependencies
        run: uv sync --locked --extra all
      - name: Run prek - pre-commit
        id: precommit
        run: uvx prek run --from-ref origin/${GITHUB_BASE_REF} --to-ref HEAD --show-diff-on-failure

# -*- coding: utf-8 -*-
import json
# standard.
import os

import certifi
# Dependencies
import urllib3
from botocore.credentials import CredentialProvider, RefreshableCredentials
from botocore.exceptions import CredentialRetrievalError
from dateutil.parser import parse

from .sts_element import STSElement


class ClientGrantsCredentialProvider(CredentialProvider):
    """

        id("${asDocsTestId(model, os)}_Trigger")
        name = docsTestProject.name + " (Trigger)"
        type = Type.COMPOSITE

        applyDefaultSettings()

        dependencies {
            snapshotDependencies(docsTestProject.docsTests)
        }
    })

enum class DocsTestType(
    val ccEnabled: Boolean,
    val docsTestName: String,
    val docsTestDesc: String,
) {

package org.apache.maven.api.di;

import java.lang.annotation.Documented;
import java.lang.annotation.Retention;

import static java.lang.annotation.RetentionPolicy.RUNTIME;

/**
 * Provides a unique identifier for dependencies when multiple implementations
 * of the same type are available.
 * <p>
 * This annotation can be used in conjunction with {@link Inject} to specify
 * which implementation should be injected when multiple candidates exist.

register_toolchains("@rules_ml_toolchain//cc:linux_aarch64_linux_aarch64")

register_toolchains("@rules_ml_toolchain//cc:linux_aarch64_linux_aarch64_cuda")

# Initialize the TensorFlow repository and all dependencies.
#
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.

			</plugin>
			<plugin>
				<groupId>net.revelc.code.formatter</groupId>
				<artifactId>formatter-maven-plugin</artifactId>
			</plugin>
		</plugins>
	</build>
	<dependencies>
		<dependency>
			<groupId>org.codelibs</groupId>
			<artifactId>corelib</artifactId>
			<version>${corelib.version}</version>
		</dependency>
		<dependency>
			<groupId>commons-codec</groupId>

        }
        return getBasedir().resolve(dir);
    }

    /**
     * {@return the project direct dependencies (directly specified or inherited)}.
     */
    @Nonnull
    List<DependencyCoordinates> getDependencies();

    /**
     * {@return the project managed dependencies (directly specified or inherited)}.
     */
    @Nonnull
    List<DependencyCoordinates> getManagedDependencies();

If you want to secure your API, there are several better things you can do, for example:

* Make sure you have well defined Pydantic models for your request bodies and responses.
* Configure any required permissions and roles using dependencies.
* Never store plaintext passwords, only password hashes.
* Implement and use well-known cryptographic tools, like pwdlib and JWT tokens, etc.
* Add more granular permission controls with OAuth2 scopes where needed.
* ...etc.

Um mehr darüber zu erfahren, siehe den Abschnitt [Benchmarks](https://fastapi.tiangolo.com/de/benchmarks/).

## Abhängigkeiten { #dependencies }

FastAPI hängt von Pydantic und Starlette ab.

### `standard`-Abhängigkeiten { #standard-dependencies }

Wenn Sie FastAPI mit `pip install "fastapi[standard]"` installieren, kommt es mit der `standard`-Gruppe optionaler Abhängigkeiten:

Verwendet von Pydantic:

Чтобы узнать больше, см. раздел [Бенчмарки](https://fastapi.tiangolo.com/ru/benchmarks/).

## Зависимости { #dependencies }

FastAPI зависит от Pydantic и Starlette.

### Зависимости `standard` { #standard-dependencies }

Когда вы устанавливаете FastAPI с помощью `pip install "fastapi[standard]"`, он идёт с группой опциональных зависимостей `standard`:

Используется Pydantic: