code: def _load_votes(counts_file, baseline_file, queries):
      counts_file_expanded = os.path.expanduser(counts_file)
      print("Reading raw votes from " + counts_file_expanded)
      sys.stdout.flush()
  
      votes = np.load(counts_file_expanded)
      print("Shape of the votes matrix = {}".format(votes.shape))
  
      if baseline_file is not None:
          baseline_file_expanded = os.path.expanduser(baseline_file)
          print(
              "Reading baseline values from " + baseline_file_expanded
          )
          sys.stdout.flush()
          baseline = np.load(baseline_file_expanded)
          if votes.shape != baseline.shape:
              raise ValueError(
                  "Counts file and baseline file must have the same shape. Got {} and "
                  "{} instead.".format(votes.shape, baseline.shape)
              )
      else:
          baseline = np.zeros_like(votes)
  
      if queries is not None:
          if votes.shape[0] < queries:
              raise ValueError(
                  "Expect {} rows, got {} in {}".format(
                      queries, votes.shape[0], counts_file
                  )
              )
          # Truncate the votes matrix to the number of queries made.
          votes = votes[:queries,]
          baseline = baseline[:queries,]
      else:
          print(
              "Process all {} input rows. (Use --queries flag to truncate.)".format(
                  votes.shape[0]
              )
          )
  
      return votes, baseline
  
file_path: privacy/research/pate_2018/ICLR2018/smooth_sensitivity_table.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import os
  import sys
  from absl import app
  from absl import flags
  import numpy
  import core
  import smooth_sensitivity
label_desc: Use image node_id 4 for calling a global function with example usage: _load_votes(counts_file, baseline_file, queries) and returns: votes,  baseline