int datasetSize;

  @Param QuantilesAlgorithm algorithm;

  private final double[][] datasets = new double[0x100][];

  @BeforeExperiment
  void setUp() {
    Random rng = new Random();
    for (int i = 0; i < 0x100; i++) {
      datasets[i] = new double[datasetSize];
      for (int j = 0; j < datasetSize; j++) {
        datasets[i][j] = rng.nextDouble();
      }
    }
  }

  private double[] dataset(int i) {

     * @return The data class.
     */
    public Class<?> getDataClass() {
        return dataClass;
    }

    /**
     * Sets the data class.
     * @param dataClass The data class to set.
     */
    public void setDataClass(final Class<?> dataClass) {
        this.dataClass = dataClass;
    }

    }

    /**
     * Returns the dataClass.
     *
     * @return the dataClass
     */
    public Class<?> getDataClass() {
        return dataClass;
    }

    /**
     * Sets the dataClass.
     *
     * @param dataClass the dataClass to set
     */
    public void setDataClass(final Class<?> dataClass) {
        this.dataClass = dataClass;
    }

    /**

You can also use `dataclasses` in the `response_model` parameter:

{* ../../docs_src/dataclasses/tutorial002.py hl[1,7:13,19] *}

The dataclass will be automatically converted to a Pydantic dataclass.

This way, its schema will show up in the API docs user interface:

<img src="/img/tutorial/dataclasses/image01.png">

     */
    private static byte[] createBuffer(int dataLen, byte[] dataBytes, String params) throws UnsupportedEncodingException {
        final byte[] paramBytes = params.getBytes(SmbConstants.DEFAULT_OEM_ENCODING);
        byte[] buffer = new byte[10 + paramBytes.length + dataLen];
        // copy params
        System.arraycopy(paramBytes, 0, buffer, 10, paramBytes.length);

///

## Dataclasses en `response_model`

También puedes usar `dataclasses` en el parámetro `response_model`:

{* ../../docs_src/dataclasses/tutorial002.py hl[1,7:13,19] *}

El dataclass será automáticamente convertido a un dataclass de Pydantic.

De esta manera, su esquema aparecerá en la interfaz de usuario de la documentación de la API:

<img src="/img/tutorial/dataclasses/image01.png">

///

## Dataclasses em `response_model`

Você também pode usar `dataclasses` no parâmetro `response_model`:

{* ../../docs_src/dataclasses/tutorial002.py hl[1,7:13,19] *}

A dataclass será automaticamente convertida para uma dataclass Pydantic.

Dessa forma, seu esquema aparecerá na interface de documentação da API:

<img src="/img/tutorial/dataclasses/image01.png">

## Dataclasses em Estruturas de Dados Aninhadas

    assertThat(manyValuesAccumulatorByAddAllPartitionedPairedStats.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / MANY_VALUES_COUNT);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStatsAccumulator accumulator =

                dstIndex += 4;
                SMBUtil.writeInt4(0, dst, dstIndex);
                dstIndex += 4; // Reserved
                final int dataLen = size8(nc.encode(dst, dstIndex));
                SMBUtil.writeInt2(dataLen, dst, lenOffset);
                dstIndex += dataLen;
            }
        }
        return dstIndex - start;
    }

    /**
     * {@inheritDoc}
     *

        final Object obj = xpathTransformer.getData(accessResultDataImpl);
        assertNull(obj);
    }

    public void test_dataClass() {
        assertNull(xpathTransformer.dataClass);
        assertEquals(Map.class, xpathMapTransformer.dataClass);
    }

    public void test_getData_dataMap() throws Exception {
        final String value = "<?xml version=\"1.0\"?>\n"//
                + "<doc>\n"//