code: def fast_match_sequences(
      a,
      b,
      a_start=0,
      a_end=None,
      b_start=0,
      b_end=None,
      min_match_length=3,
      max_recursion_depth=128,
  ):
      """Compute diffs between two sequences.
  
      This function is similar in functionality and spirit to
      difflib.SequenceMatcher.get_opcodes, but it seems to run faster.
  
      if a_start, a_end, b_start, b_end are specified, then we compute diffs of
      the segments a[a_start:a_end] and b[b_start:b_end].  Returned indices
      are relative to the full sequence.
  
      We try to match the longest matching segments first, but due to heuristics
      in finding the matches, this is not guaranteed.
  
      Matching segments shorter than min_match_length are counted as part of the
      surrounding differing segments, unless they are at the beginning or end of
      both sequences.  This helps eliminate junk matches.
  
      Args:
        a: a sequence
        b: a sequence
        a_start: an optional integer
        a_end: an optional integer
        b_start: an optional integer
        b_end: an optional integer
        min_match_length: an integer
        max_recursion_depth: an integer - avoids crashes in weird corner cases
          involving pairs of long repetitive sequences.
  
      Returns:
        a list of 5-tuples (tag, i1, i2, j1, j2).
        Each tuple represents the alignment of segment a[i1:i2] with b[j1:j2].
          tag is either "equal" or "diff".  Note that the tags differ from those
          returned by difflib.SequenceMatcher.get_opcodes.
      """
      if a_end is None:
          a_end = len(a)
      if b_end is None:
          b_end = len(b)
      if a_start == a_end and b_start == b_end:
          return []
      if a_start == a_end or b_start == b_end:
          return [("diff", a_start, a_end, b_start, b_end)]
      # Compute an index from value to first occurrence in the b segment.
      # Technically, we should index and explore all occurrences of a value,
      # but that might be much slower.
      b_index = {}
      for j in range(b_end - 1, b_start - 1, -1):
          b_index[b[j]] = j
      # we will look for the longest match we can find.
      max_match_length = 0
      a_pos = a_start
      while a_pos < a_end:
          val = a[a_pos]
          b_pos = b_index.get(val)
          if b_pos is None:
              a_pos += 1
              continue
          else:
              a_match_start = a_pos
              a_match_end = a_pos + 1
              b_match_start = b_pos
              b_match_end = b_pos + 1
              while (
                  a_match_start > a_start
                  and b_match_start > b_start
                  and a[a_match_start - 1] == b[b_match_start - 1]
              ):
                  a_match_start -= 1
                  b_match_start -= 1
              while (
                  a_match_end < a_end
                  and b_match_end < b_end
                  and a[a_match_end] == b[b_match_end]
              ):
                  a_match_end += 1
                  b_match_end += 1
              # Compute the length of the matching segment.  We prefer the longest.
              match_length = a_match_end - a_match_start
              # Extra credit for matching at the beginning or end of the sequence.
              if a_match_start == 0 and b_match_start == 0:
                  match_length += min_match_length
              if a_match_end == len(a) and b_match_end == len(b):
                  match_length += min_match_length
              if match_length > max_match_length:
                  max_match_length = match_length
                  best_match = (
                      a_match_start,
                      a_match_end,
                      b_match_start,
                      b_match_end,
                  )
              # advance a_pos to the end of this match to avoid wasting time
              # rediscovering this match.
              a_pos = a_match_end
      if (
          max_match_length < min_match_length
          or max_recursion_depth == 0
      ):
          return [("diff", a_start, a_end, b_start, b_end)]
      (
          a_match_start,
          a_match_end,
          b_match_start,
          b_match_end,
      ) = best_match
      return (
          fast_match_sequences(
              a,
              b,
              a_start,
              a_match_start,
              b_start,
              b_match_start,
              min_match_length,
              max_recursion_depth - 1,
          )
          + [
              (
                  "equal",
                  a_match_start,
                  a_match_end,
                  b_match_start,
                  b_match_end,
              )
          ]
          + fast_match_sequences(
              a,
              b,
              a_match_end,
              a_end,
              b_match_end,
              b_end,
              min_match_length,
              max_recursion_depth - 1,
          )
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import os
  import random
  import re
  import subprocess
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import text_encoder
  import tensorflow.compat.v1
label_desc: Use image node_id 26 for calling a global function with example usage: fast_match_sequences(a, b, a_start, a_end, b_start, b_end, min_match_length, max_recursion_depth) and returns: unknown, list, list, list