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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _GetEntries(self, paths, max_entries, iterator_from_file, is_sequence=False):
 """Extracts examples into a dictionary of feature values. Args: paths: A list of the paths to the files to parse. max_entries: The maximum number of examples to load. iterator_from_file: A method that takes a file path string and returns an iterator to the examples in that file. is_sequence: True if the input data from 'iterator_from_file' are tf.SequenceExamples, False if tf.Examples. Defaults to false. Returns: A tuple with two elements: - A dictionary of all features parsed thus far and arrays of their values. - The number of examples parsed. """

    entries = {}
    index = 0
    for filepath in paths:
      reader = iterator_from_file(filepath)
      for record in reader:
        if is_sequence:
          sequence_example = tf.train.SequenceExample.FromString(record)
          self._ParseExample(sequence_example.context.feature,
                             sequence_example.feature_lists.feature_list,
                             entries, index)
        else:
          self._ParseExample(
              tf.train.Example.FromString(record).features.feature, [], entries,
              index)
        index += 1
        if index == max_entries:
          return entries, index
    return entries, index