class_docstr: A generator which tracks the min/max list length diffs for two paths.
class_docstr_tok: ["A","generator","which","tracks","the","min\/max","list","length","diffs","for","two","paths","."]
docstr: Returns the length differences as the tuple (min_diff, max_diff).
  
  If no rows have ever been observed in which all the `required_paths` were
  non-null, the min and max will be set to 0.
  
  Args:
    accumulator: The input accumulator of the form (min_diff, max_diff).
  
  Returns:
    A tuple of (min_diff, max_diff).
docstr_tok: ["Returns","the","length","differences","as","the","tuple","(","min_diff",",","max_diff",")",".","If","no","rows","have","ever","been","observed","in","which","all","the","`","required_paths","`","were","non-null",",","the","min","and","max","will","be","set","to","0",".","Args",":","accumulator",":","The","input","accumulator","of","the","form","(","min_diff",",","max_diff",")",".","Returns",":","A","tuple","of","(","min_diff",",","max_diff",")","."]
code: def extract_output(self, accumulator: MinMax) -> MinMax:
      """Returns the length differences as the tuple (min_diff, max_diff).
  
      If no rows have ever been observed in which all the `required_paths` were
      non-null, the min and max will be set to 0.
  
      Args:
        accumulator: The input accumulator of the form (min_diff, max_diff).
  
      Returns:
        A tuple of (min_diff, max_diff).
      """
      min_diff, max_diff = accumulator
      min_diff = min_diff if min_diff != float("inf") else 0
      max_diff = max_diff if max_diff != float("-inf") else 0
      return (min_diff, max_diff)
  
file_path: data-validation/tensorflow_data_validation/statistics/generators/constituents/length_diff_generator.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from tensorflow_data_validation import types
  from tensorflow_data_validation.statistics.generators import input_batch
  from tensorflow_data_validation.statistics.generators import stats_generator
  from typing import Iterable, Optional, Text, Tuple, Union
label_desc: Use image node_id 7 for calling the LengthDiffGenerator obj's underlying member method code with example usage: obj.extract_output(accumulator) and returns: min_diff,  max_diff