abstract: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture...

{* ../../docs_src/security/tutorial001_an_py39.py hl[12] *}

Но это всё ещё не слишком полезно.

Сделаем так, чтобы она возвращала текущего пользователя.

## Создать модель пользователя { #create-a-user-model }

Сначала создадим Pydantic-модель пользователя.

Точно так же, как мы используем Pydantic для объявления тел запросов, мы можем использовать его где угодно:

import org.apache.maven.api.di.Singleton;
import org.apache.maven.api.model.Build;
import org.apache.maven.api.model.Model;
import org.apache.maven.api.model.Parent;
import org.apache.maven.api.model.Plugin;
import org.apache.maven.api.model.PluginManagement;
import org.apache.maven.api.model.Repository;
import org.apache.maven.api.model.RepositoryPolicy;
import org.apache.maven.api.services.ModelBuilder;

					association.Error = associationDB.Model(nil).Where(clause.IN{Column: column, Values: values}).Delete(reflect.New(rel.FieldSchema.ModelType).Interface()).Error
				}
			}
		case schema.HasOne, schema.HasMany:
			model := reflect.New(rel.FieldSchema.ModelType).Interface()
			tx := association.DB.Model(model)

import gradlebuild.basics.classanalysis.Attributes.minified
import gradlebuild.basics.transforms.Minify
import org.gradle.api.internal.attributes.AttributesFactory
import org.gradle.internal.component.external.model.DefaultModuleComponentSelector
import org.gradle.kotlin.dsl.support.serviceOf

/**
 * A map from artifact name to a set of class name prefixes that should be kept.

                if annotation_is_pydantic_v1(model):
                    raise PydanticV1NotSupportedError(
                        "pydantic.v1 models are no longer supported by FastAPI."
                        f" In responses={{}}, please update {model}."
                    )
                response_field = create_model_field(
                    name=response_name, type_=model, mode="serialization"
                )

            <option value="$PROJECT_DIR$/platforms/core-configuration/kotlin-dsl-tooling-models" />
            <option value="$PROJECT_DIR$/platforms/core-configuration/model-core" />
            <option value="$PROJECT_DIR$/platforms/core-configuration/model-groovy" />
            <option value="$PROJECT_DIR$/platforms/core-configuration/model-reflect" />
            <option value="$PROJECT_DIR$/platforms/core-configuration/project-features" />

import org.apache.maven.model.Activation;
import org.apache.maven.model.Build;
import org.apache.maven.model.Dependency;
import org.apache.maven.model.DependencyManagement;
import org.apache.maven.model.InputLocation;
import org.apache.maven.model.InputLocationTracker;
import org.apache.maven.model.InputSource;
import org.apache.maven.model.Model;
import org.apache.maven.model.Parent;
import org.apache.maven.model.Plugin;

 ╭────────── FastAPI CLI - Development mode ───────────╮
 │                                                     │
 │  Serving at: http://127.0.0.1:8000                  │
 │                                                     │
 │  API docs: http://127.0.0.1:8000/docs               │
 │                                                     │
 │  Running in development mode, for production use:   │

    Undefined,
    get_compat_model_name_map,
    get_definitions,
    get_schema_from_model_field,
    lenient_issubclass,
)
from fastapi.datastructures import DefaultPlaceholder
from fastapi.dependencies.models import Dependant
from fastapi.dependencies.utils import (
    _get_flat_fields_from_params,
    get_flat_dependant,
    get_flat_params,
    get_validation_alias,
)
from fastapi.encoders import jsonable_encoder