class_docstr: Base class for generator of stats proto from TF data.
class_docstr_tok: ["Base","class","for","generator","of","stats","proto","from","TF","data","."]
docstr: 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.
docstr_tok: ["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","."]
code: 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
  
file_path: facets/facets_overview/python/base_feature_statistics_generator.py
imports: from functools import partial
  import warnings
  from base_generic_feature_statistics_generator import BaseGenericFeatureStatisticsGenerator
  import tensorflow
label_desc: Use image node_id 4 for calling the BaseFeatureStatisticsGenerator obj's underlying member method code with example usage: obj._GetEntries(paths, max_entries, iterator_from_file, is_sequence) and returns: entries,  index, entries,  index