assertEquals(1, item.getInputs().length);
        assertEquals("", item.getInputs()[0]);
        assertEquals("output", item.getOutput());
    }

    // Test reload with multiple inputs
    @Test
    public void test_reload_multipleInputs() throws Exception {
        writeTestFile("a,b,c,d,e => output\n");

        PagingList<CharMappingItem> result = charMappingFile.selectList(0, 10);

                    .hasArg()
                    .desc("Log file where all build output will go (disables output color)")
                    .get());
            options.addOption(Option.builder()
                    .longOpt(RAW_STREAMS)
                    .desc("Do not decorate standard output and error streams")
                    .get());
            options.addOption(Option.builder(SHOW_VERSION)

type testLogger struct {
	current atomic.Pointer[testing.TB]
	action  atomic.Int32
}

// SetLogTB will set the logger to output to tb.
// Call the returned function to disable logging.
func (t *testLogger) SetLogTB(tb testing.TB) func() {
	return t.setTB(tb, logMessage)
}

// SetErrorTB will set the logger to output to tb.Error.
// Call the returned function to disable logging.
func (t *testLogger) SetErrorTB(tb testing.TB) func() {

		{Bucket: "test-getobjectinfo", Name: "Asia/empty-dir/", ContentType: "application/octet-stream", IsDir: true},
	}
	testCases := []struct {
		bucketName string
		objectName string

		// Expected output of GetObjectInfo.
		result ObjectInfo
		err    error
		// Flag indicating whether the test is expected to pass or not.
		shouldPass bool
	}{
		// Test cases with invalid bucket names ( Test number 1-4 ).

Sie erhalten eine Response wie:

```json
{
  "description": "Some data",
  "content": "hello"
}
```

## Pydantic `bytes` für Ausgabedaten { #pydantic-bytes-for-output-data }

Sie können in der Modellkonfiguration für Ausgabedaten auch `bytes`-Felder mit `ser_json_bytes` verwenden; Pydantic wird die Bytes bei der Erzeugung der JSON-Response als base64 *serialisieren*.

  "content": "hello"
}
```

## Pydantic `bytes` 用於輸出資料 { #pydantic-bytes-for-output-data }

你也可以在模型設定中搭配 `ser_json_bytes` 使用 `bytes` 欄位來處理輸出資料；當產生 JSON 回應時，Pydantic 會將位元組以 base64 進行序列化。

{* ../../docs_src/json_base64_bytes/tutorial001_py310.py ln[1:2,12:16,29,38:41] hl[16] *}

## Pydantic `bytes` 用於輸入與輸出資料 { #pydantic-bytes-for-input-and-output-data }

      value:
      render: shell
    validations:
      required: true
  - type: textarea
    id: logs
    attributes:
      label: Relevant log output
      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.

	for _, test := range lexTests {
		input := NewInput(test.name)
		input.Push(NewTokenizer(test.name, strings.NewReader(test.input), nil))
		result := drain(input)
		if result != test.output {
			t.Errorf("%s: got %q expected %q", test.name, result, test.output)
		}
	}
}

// lines joins the arguments together as complete lines.
func lines(a ...string) string {
	return strings.Join(a, "\n") + "\n"
}

    if (collection == null) {
      return createUnmodifiableEmptyCollection();
    }

    Collection<V> output = createCollection();
    output.addAll(collection);
    totalSize -= collection.size();
    collection.clear();

    return unmodifiableCollectionSubclass(output);
  }

  <E extends @Nullable Object> Collection<E> unmodifiableCollectionSubclass(
      Collection<E> collection) {

Recibirás una response como:

```json
{
  "description": "Some data",
  "content": "hello"
}
```

## Pydantic `bytes` para datos de salida { #pydantic-bytes-for-output-data }

También puedes usar campos `bytes` con `ser_json_bytes` en la configuración del modelo para datos de salida, y Pydantic serializará los bytes como base64 al generar la response JSON.