<abbr title="too long; didn't read"><strong>TL;DR:</strong></abbr>

If you are using third party libraries that tell you to call them with `await`, like:

```Python
results = await some_library()
```

Then, declare your *path operation functions* with `async def` like:

```Python hl_lines="2"
@app.get('/')
async def read_results():
    results = await some_library()
    return results
```

/// note

{* ../../docs_src/path_operation_configuration/tutorial003_py310.py hl[18:19] *}

## Description from docstring { #description-from-docstring }

As descriptions tend to be long and cover multiple lines, you can declare the *path operation* description in the function <abbr title="a multi-line string as the first expression inside a function (not assigned to any variable) used for documentation">docstring</abbr> and **FastAPI** will read it from there.

/// tip

If you have strict type checks in your editor, mypy, etc, you can declare the function return type as `Any`.

That way you tell the editor that you are intentionally returning anything. But FastAPI will still do the data documentation, validation, filtering, etc. with the `response_model`.

///

### `response_model` Priority { #response-model-priority }

## Dependency Injection { #dependency-injection }

Using `BackgroundTasks` also works with the dependency injection system, you can declare a parameter of type `BackgroundTasks` at multiple levels: in a *path operation function*, in a dependency (dependable), in a sub-dependency, etc.

Essas funções podem ser declaradas com `async def` ou `def` normal.

### Evento `startup`

Para adicionar uma função que deve rodar antes da aplicação iniciar, declare-a com o evento `"startup"`:

{* ../../docs_src/events/tutorial001.py hl[8] *}

Nesse caso, a função de manipulação de evento `startup` irá inicializar os itens do "banco de dados" (só um `dict`) com alguns valores.

// A TokenReader is like a reader, but returns lex tokens of type Token. It also can tell you what
// the text of the most recently returned token is, and where it was found.
// The underlying scanner elides all spaces except newline, so the input looks like a stream of
// Tokens; original spacing is lost but we don't need it.
type TokenReader interface {
	// Next returns the next token.
	Next() ScanToken

import org.apache.maven.api.toolchain.ToolchainModel;

/**
 * Represents a toolchain in the Maven build system.
 *
 * <p>A toolchain is a set of tools that can be used to build a project.
 * This interface allows users to define and configure various toolchains
 * that can be utilized by Maven during the build process. Toolchains can
 * include compilers, interpreters, and other tools that are necessary

if [[ "${TFCI_MACOS_BAZEL_TEST_DIR_ENABLE}" == 1 ]]; then
  mkdir -p "${TFCI_MACOS_BAZEL_TEST_DIR_PATH}"
  export TEST_TMPDIR="${TFCI_MACOS_BAZEL_TEST_DIR_PATH}"
fi

# "TFCI_MACOS_INSTALL_BAZELISK_ENABLE" is used to decide if we need to install
# Bazelisk manually. We enable this for macOS x86 builds as those VMs do not
# have Bazelisk pre-installed. "TFCI_MACOS_INSTALL_BAZELISK_URL" contains the

						fatalf("internal error: ParseGo: abspath contains unexpected newline character: %q", abspath)
					}
					f.Preamble += fmt.Sprintf("#line %d %q\n", sourceLine(cg), abspath)
					f.Preamble += commentText(cg) + "\n"
					f.Preamble += "#line 1 \"cgo-generated-wrapper\"\n"
				}
			}

		case *ast.FuncDecl:
			// Also, reject attempts to declare methods on C.T or *C.T.
			// (The generated code would otherwise accept this

Because each SQLModel model is also a Pydantic model, you can use it in the same **type annotations** that you could use Pydantic models.

For example, if you declare a parameter of type `Hero`, it will be read from the **JSON body**.

The same way, you can declare it as the function's **return type**, and then the shape of the data will show up in the automatic API docs UI.