data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }
    expected_response = data.copy()
    expected_response.update(
        {
            "start_process": "2018-12-22T14:05:00+00:00",
            "duration": 176_100,
            "item_id": item_id,
        }
    )

    data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }
    expected_response = data.copy()
    expected_response.update(
        {
            "start_process": "2018-12-22T14:05:00+00:00",
            "duration": 176_100,
            "item_id": item_id,
        }
    )

    data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }
    expected_response = data.copy()
    expected_response.update(
        {
            "start_process": "2018-12-22T14:05:00+00:00",
            "duration": 176_100,
            "item_id": item_id,
        }
    )

    data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }
    expected_response = data.copy()
    expected_response.update(
        {
            "start_process": "2018-12-22T14:05:00+00:00",
            "duration": 176_100,
            "item_id": item_id,
        }
    )

    data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }
    expected_response = data.copy()
    expected_response.update(
        {
            "start_process": "2018-12-22T14:05:00+00:00",
            "duration": 176_100,
            "item_id": item_id,
        }
    )

    def get_model():
        return model

    client = TestClient(app)
    with client:
        response = client.get("/model")
    assert response.json() == {"dt_field": "2019-01-01T08:00:00+00:00"}


# TODO: remove when deprecating Pydantic v1
@needs_pydanticv1
def test_pydanticv1():
    class ModelWithDatetimeField(BaseModel):
        dt_field: datetime

        class Config:

    model = ModelWithCustomEncoder(dt_field=datetime(2019, 1, 1, 8))
    assert jsonable_encoder(model) == {"dt_field": "2019-01-01T08:00:00+00:00"}
    subclass_model = ModelWithCustomEncoderSubclass(dt_field=datetime(2019, 1, 1, 8))
    assert jsonable_encoder(subclass_model) == {"dt_field": "2019-01-01T08:00:00+00:00"}


# TODO: remove when deprecating Pydantic v1
@needs_pydanticv1
def test_encode_custom_json_encoders_model_pydanticv1():

	<include path="crawler/container.xml" />
	<component name="tikaExtractor"
		class="org.codelibs.fess.crawler.extractor.impl.TikaExtractor">
		<property name="maxCompressionRatio">2</property>
		<property name="maxUncompressionSize">10000000</property>
	</component>

  fun largeRead() {
    val data = randomByteString(1000000)
    write(data)
    val operator =
      FileOperator(
        randomAccessFile!!.getChannel(),
      )
    val buffer = Buffer()
    operator.read(0, buffer, data.size.toLong())
    assertThat(buffer.readByteString()).isEqualTo(data)
  }

  @Test
  fun largeWrite() {
    val data = randomByteString(1000000)
    val operator =
      FileOperator(

  }

  public void testAutoIncrementStep_millis() {
    FakeTicker ticker = new FakeTicker().setAutoIncrementStep(1, MILLISECONDS);
    assertEquals(0, ticker.read());
    assertEquals(1000000, ticker.read());
    assertEquals(2000000, ticker.read());
  }

  public void testAutoIncrementStep_seconds() {
    FakeTicker ticker = new FakeTicker().setAutoIncrementStep(3, SECONDS);
    assertEquals(0, ticker.read());