assertThat(body!!.contentType().toString()).isEqualTo(
      "application/x-www-form-urlencoded; charset=utf-8",
    )
    assertThat(bodyAsString(body)).isEqualTo("foo")
  }

  @Test
  fun dataPut() {
    val request = fromArgs("-d", "foo", "-X", "PUT", "http://example.com").createRequest()
    val body = request.body
    assertThat(request.method).isEqualTo("PUT")

  private double[] dataset;

  @Override
  protected void setUp() {
    dataset = new double[DATASET_SIZE];
    for (int i = 0; i < DATASET_SIZE; i++) {
      dataset[i] = RNG.nextDouble();
    }
  }

  public void testSingleQuantile_median() {
    double referenceValue = REFERENCE_ALGORITHM.singleQuantile(1, 2, dataset.clone());

      for (int j = 0; j < datasetSize; j++) {
        datasets[i][j] = rng.nextDouble();
      }
    }
  }

  private double[] dataset(int i) {
    // We must test on a fresh clone of the dataset each time. Doing sorts and quickselects on a
    // dataset which is already sorted or partially sorted is cheating.
    return datasets[i & 0xFF].clone();
  }

  @Benchmark
  double median(int reps) {

      for (int j = 0; j < datasetSize; j++) {
        datasets[i][j] = rng.nextDouble();
      }
    }
  }

  private double[] dataset(int i) {
    // We must test on a fresh clone of the dataset each time. Doing sorts and quickselects on a
    // dataset which is already sorted or partially sorted is cheating.
    return datasets[i & 0xFF].clone();
  }

  @Benchmark
  double median(int reps) {

    @Override
    double singleQuantile(int index, int scale, double[] dataset) {
      Arrays.sort(dataset);
      return singleQuantileFromSorted(index, scale, dataset);
    }

    @Override
    Map<Integer, Double> multipleQuantiles(
        Collection<Integer> indexes, int scale, double[] dataset) {
      Arrays.sort(dataset);
      ImmutableMap.Builder<Integer, Double> builder = ImmutableMap.builder();

  private double[] dataset;

  @Override
  protected void setUp() {
    dataset = new double[DATASET_SIZE];
    for (int i = 0; i < DATASET_SIZE; i++) {
      dataset[i] = RNG.nextDouble();
    }
  }

  public void testSingleQuantile_median() {
    double referenceValue = REFERENCE_ALGORITHM.singleQuantile(1, 2, dataset.clone());

            .add(SHORT_NAME, 13.0f)
            .add(LONG_NAME, 14.0f)
            .addValue(15.0f);
      }
    };

    void addEntries(MoreObjects.ToStringHelper helper) {}
  }

  @Param Dataset dataset;

  private static final String SHORT_NAME = "userId";
  private static final String LONG_NAME = "fluxCapacitorFailureRate95Percentile";

  private MoreObjects.ToStringHelper newHelper() {

            .add(SHORT_NAME, 13.0f)
            .add(LONG_NAME, 14.0f)
            .addValue(15.0f);
      }
    };

    void addEntries(MoreObjects.ToStringHelper helper) {}
  }

  @Param Dataset dataset;

  private static final String SHORT_NAME = "userId";
  private static final String LONG_NAME = "fluxCapacitorFailureRate95Percentile";

  private MoreObjects.ToStringHelper newHelper() {

      }
    }
  }
  const float* data_in = static_cast<const float*>(dltensor_in->data);
  const float* data_out = static_cast<const float*>(dltensor_out->data);
  for (size_t j = 0; j < num_elements; ++j) {
    EXPECT_EQ(data_out[j], data_in[j]);
  }

  TFE_CallDLManagedTensorDeleter(dlm_out);
  TFE_DeleteTensorHandle(handle);
}

TEST(DLPack, HandleFromDLPackStrides) {

MinIO's Object Lambda implementation allows for transforming your data to serve unique data format requirements for each application. For example, a dataset created by an ecommerce application might include personally identifiable information (PII). When the same data is processed for analytics, PII should be redacted. However, if the same dataset is used for a marketing campaign, you might need to enrich the data with additional details, such as information from the customer loyalty database.