}

  /**
   * Read 100 MiB of 'a' chars. This was really slow due to a performance bug in [MultipartReader],
   * and will be really slow if we regress the fix for that.
   */
  @Test
  fun `reading a large part with small byteCount`() {
    val multipartBody =
      MultipartBody
        .Builder("foo")
        .addPart(
          headersOf("header-name", "header-value"),
          object : RequestBody() {

   ({{{./xref/org/apache/maven/model/validation/DefaultModelValidator.html}source}})

   []

 * phase 2, with optional plugin processing

   ** Build up a raw model by re-reading the file and enriching it based on information available in the reactor. Some features:

      *** Resolve version of versionless parents based on relativePath (including ci-friendly versions)

     * must be between 1 and [Integer.MAX_VALUE] when converted to milliseconds.
     *
     * The call timeout spans the entire call: resolving DNS, connecting, writing the request body,
     * server processing, and reading the response body. If the call requires redirects or retries
     * all must complete within one timeout period.
     *
     * The default value is 0 which imposes no timeout.
     */
    fun callTimeout(

{* ../../docs_src/settings/app02_an_py310/test_main.py hl[9:10,13,21] *}

依存関係オーバーライドでは、新しい `Settings` オブジェクトを作る際に `admin_email` に新しい値を設定し、その新しいオブジェクトを返します。

そして、それが使用されていることをテストできます。

## `.env` ファイルの読み込み { #reading-a-env-file }

変更が多くなりそうな設定が多数ある場合、環境ごとにファイルに入れて、環境変数としてそこから読み込むと便利なことがあります。

このプラクティスは十分に一般的で名前もあり、これらの環境変数は通常 `.env` というファイルに置かれ、そのファイルは「dotenv」と呼ばれます。

/// tip | 豆知識

	}
}

var s2DecPool = bpool.Pool[*s2.Reader]{New: func() *s2.Reader {
	// Default alloc block for network transfer.
	return s2.NewReader(nil, s2.ReaderAllocBlock(16<<10))
}}

// metacacheReader allows reading a cache stream.
type metacacheReader struct {
	mr      *msgp.Reader
	current metaCacheEntry
	err     error // stateful error
	closer  func()
	creator func() error
}

    return {};
  }
  const auto& fdef_lib = gdef.library();
  if (fdef_lib.gradient_size() > 0) {
    status->status = tensorflow::errors::Internal(
        "GradientDef is not supported in reading Dataset related functions: ",
        text_proto);
    return {};
  }
  std::vector<UniqueFuncPtr> ret;
  for (const FunctionDef& fdef : fdef_lib.function()) {
    // Make a copy so that we can mutate it.

		// Put it back and read it again.
		if err = r.UnreadRune(); err != nil {
			t.Fatal("unexpected error on UnreadRune:", err)
		}
		r2, _, err := r.ReadRune()
		if err != nil {
			t.Fatal("unexpected error reading after unreading:", err)
		}
		if r1 != r2 {
			t.Fatalf("incorrect rune after unread: got %c, want %c", r1, r2)
		}
	}
	if got != want {
		t.Errorf("got %q, want %q", got, want)
	}
}

Na sobrescrita da dependência definimos um novo valor para `admin_email` ao criar o novo objeto `Settings`, e então retornamos esse novo objeto.

Depois podemos testar que ele é usado.

## Lendo um arquivo `.env` { #reading-a-env-file }

Se você tiver muitas configurações que possivelmente mudam bastante, talvez em diferentes ambientes, pode ser útil colocá-las em um arquivo e então lê-las como se fossem variáveis de ambiente.

Les « Context Managers » sont des objets Python que vous pouvez utiliser dans une instruction `with`.

Par exemple, [vous pouvez utiliser `with` pour lire un fichier](https://docs.python.org/3/tutorial/inputoutput.html#reading-and-writing-files) :

```Python
with open("./somefile.txt") as f:
    contents = f.read()
    print(contents)
```

En coulisse, `open("./somefile.txt")` crée un objet appelé « Context Manager ».

	// Object operations.

	// GetObjectNInfo returns a GetObjectReader that satisfies the
	// ReadCloser interface. The Close method runs any cleanup
	// functions, so it must always be called after reading till EOF
	//
	// IMPORTANTLY, when implementations return err != nil, this
	// function MUST NOT return a non-nil ReadCloser.