func (w *window) appendTo(buf []byte, from, to uint32) []byte {
	dataLen := uint32(len(w.data))
	from += uint32(w.off)
	to += uint32(w.off)

	wrap := false
	if from > dataLen {
		from -= dataLen
		wrap = !wrap
	}
	if to > dataLen {
		to -= dataLen
		wrap = !wrap
	}

	if wrap {
		buf = append(buf, w.data[from:]...)
		return append(buf, w.data[:to]...)

            .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() {

      )

      # The resulting dataset should not contain any tf.Tensors.
      self.assertFalse(any(map(_contains_tensor, new_repr_ds)))
      self._assert_sample_values_all_close(sess, data_gen(), new_repr_ds)

  @test_util.deprecated_graph_mode_only
  def test_replace_tensors_by_numpy_ndarrays_is_noop_when_no_tensor(self):
    # Fill the representative dataset with np.ndarrays only.

            .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() {

            reduce_dataset.getF());

    // The reduce function arguments consist of three part in this order:
    // 1. Reduction state inputs.
    // 2. Dataset inputs.
    // 3. Captures inputs.
    // The number of dataset inputs can be indirectly determined to be
    // total_number_of_inputs - state_inputs - captured_inputs.
    auto func_inputs = reduce_func.getFunctionType().getInputs();

    // global maximum of all sample datasets.
    float global_max = 2;
  }

  message AverageMinMaxStatistics {
    // sum of batch's minimum in each sample dataset.
    float min_sum = 1;

    // sum of batch's maximum in each sample dataset.
    float max_sum = 2;

    // number of sample datasets
    int32 num_samples = 3;
  }

  message HistogramStatistics {
    // width of bin

)

type testData string

func (t testData) load() (string, error) {
	by, err := validation.FS.ReadFile(path.Join("dataset", string(t)))
	if err != nil {
		return "", err
	}

	return string(by), nil
}

func loadTestData(t framework.TestContext) []testData {
	entries, err := validation.FS.ReadDir("dataset")
	if err != nil {
		t.Fatalf("Error loading test data: %v", err)
	}

	var result []testData

// TFRecord format.
// Next ID: 2
message TfRecordFile {
  string path = 1;
}

// Configures a single representative dataset used to calibrate a single
// function.
// Next ID: 3
message RepresentativeDatasetConfig {
  oneof file {
    // Represents representative dataset saved as a .tfrecord file format.
    TfRecordFile tf_record = 1;
  }

  // [TF SavedModel] Identifies a SignatureDef which represents a single

  """
  if isinstance(representative_dataset, Mapping):
    if set(signature_def_map.keys()) != set(representative_dataset.keys()):
      raise ValueError(
          'The signature keys and the keys of representative dataset map '
          f'do not match. Signature keys: {set(signature_def_map.keys())}, '
          f'representative dataset map: {set(representative_dataset.keys())}.'

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For more details, see https://telemetry.go.dev/privacy.
This data is collected in accordance with the Google Privacy Policy
(https://policies.google.com/privacy).

To view the current telemetry mode, run "go telemetry".