code: def compress_histogram(buckets, bps=NORMAL_HISTOGRAM_BPS):
      """Creates fixed size histogram by adding compression to accumulated state.
  
      This routine transforms a histogram at a particular step by linearly
      interpolating its variable number of buckets to represent their cumulative
      weight at a constant number of compression points. This significantly reduces
      the size of the histogram and makes it suitable for a two-dimensional area
      plot where the output of this routine constitutes the ranges for a single x
      coordinate.
  
      Args:
        buckets: A list of buckets, each of which is a 3-tuple of the form
          `(min, max, count)`.
        bps: Compression points represented in basis points, 1/100ths of a percent.
            Defaults to normal distribution.
  
      Returns:
        List of values for each basis point.
      """
      # See also: Histogram::Percentile() in core/lib/histogram/histogram.cc
      buckets = np.array(buckets)
      if not buckets.size:
          return [
              CompressedHistogramValue(b, 0.0).as_tuple() for b in bps
          ]
      (minmin, maxmax) = (buckets[0][0], buckets[-1][1])
      counts = buckets[:, 2]
      right_edges = list(buckets[:, 1])
      weights = (counts * bps[-1] / (counts.sum() or 1.0)).cumsum()
  
      result = []
      bp_index = 0
      while bp_index < len(bps):
          i = np.searchsorted(weights, bps[bp_index], side="right")
          while i < len(weights):
              cumsum = weights[i]
              cumsum_prev = weights[i - 1] if i > 0 else 0.0
              if (
                  cumsum == cumsum_prev
              ):  # prevent division-by-zero in `_lerp`
                  i += 1
                  continue
              if not i or not cumsum_prev:
                  lhs = minmin
              else:
                  lhs = max(right_edges[i - 1], minmin)
              rhs = min(right_edges[i], maxmax)
              weight = _lerp(
                  bps[bp_index], cumsum_prev, cumsum, lhs, rhs
              )
              result.append(
                  CompressedHistogramValue(
                      bps[bp_index], weight
                  ).as_tuple()
              )
              bp_index += 1
              break
          else:
              break
      while bp_index < len(bps):
          result.append(
              CompressedHistogramValue(bps[bp_index], maxmax).as_tuple()
          )
          bp_index += 1
      return result
  
file_path: tensorboard/tensorboard/plugins/distribution/compressor.py
imports: import dataclasses
  import numpy
  from typing import Tuple
label_desc: Use image node_id 2 for calling a global function with example usage: compress_histogram(buckets, bps) and returns: result, unknown