code: def load_statistics(log_path, iteration_number=None, verbose=True):
      """Reads in a statistics object from log_path.
  
      Args:
        log_path: string, provides the full path to the training/eval statistics.
        iteration_number: The iteration number of the statistics object we want
          to read. If set to None, load the latest version.
        verbose: Whether to output information about the load procedure.
  
      Returns:
        data: The requested statistics object.
        iteration: The corresponding iteration number.
  
      Raises:
        Exception: if data is not present.
      """
      # If no iteration is specified, we'll look for the most recent.
      if iteration_number is None:
          iteration_number = get_latest_iteration(log_path)
  
      log_file = "%s/%s_%d" % (log_path, FILE_PREFIX, iteration_number)
  
      if verbose:
          # pylint: disable=superfluous-parens
          print("Reading statistics from: {}".format(log_file))
          # pylint: enable=superfluous-parens
  
      with tf.io.gfile.GFile(log_file, "rb") as f:
          return pickle.load(f), iteration_number
  
file_path: dopamine/dopamine/colab/utils.py
imports: import itertools
  import os
  import pickle
  import sys
  import numpy
  import pandas
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: load_statistics(log_path, iteration_number, verbose) and returns: pickle,  iteration_number