d7a7dd6fe7106f6501e9995a92153f65238bbb47,tensorflow_transform/analyzers.py,QuantilesCombiner,extract_output,#QuantilesCombiner#Any#,1347

Before Change

    buckets = self._get_buckets_callable(summary)

    if not self._include_max_and_min:
      // If always_return_num_quantiles is set to True, the number of elements in
      // buckets is always equal to num_quantiles + 1. Hence we trim the min and
      // max quantile boundaries to return the internal boundaries.
      if self._always_return_num_quantiles:
        buckets = buckets[1:-1]
      // If always_return_num_quantiles is set to False, the approximate quantile
      // library can return less or more than requested number of quantiles.
      // The max value can be same as the last internal boundary, due to removal
      // of duplicates. Below, the min and/or max quantile boundaries are trimmed
      // depending on the actual boundaries returned by the library.
      elif buckets.size >= (self._num_quantiles + 1):
        // Trim min/max.
        buckets = buckets[1:-1]
      elif buckets.size == self._num_quantiles:
        buckets = buckets[1:]
      else:
        // Do not trim min/max, these are part of requested boundaries.
        pass

    return [buckets]

  def output_tensor_infos(self):
    return [
        analyzer_nodes.TensorInfo(

After Change

      return buckets

    if not self._include_max_and_min:
      bucket_lists = list(map(prune_buckets, bucket_lists))

    return [np.reshape(np.stack(bucket_lists, axis=0), output_shape)]

  def output_tensor_infos(self):
    return [
        analyzer_nodes.TensorInfo(

Instances