code: def update_learned_dfas():
      """Write any modifications to the SHRINKING_DFAS dictionary
      back to the learned DFAs file."""
  
      source = learned_dfa_file.read_text(encoding="utf-8")
  
      lines = source.splitlines()
  
      i = lines.index("# AUTOGENERATED BEGINS")
  
      del lines[i + 1 :]
  
      lines.append("")
      lines.append("# fmt: off")
      lines.append("")
  
      for k, v in sorted(SHRINKING_DFAS.items()):
          lines.append(f"SHRINKING_DFAS[{k!r}] = {v!r}  # noqa: E501")
  
      lines.append("")
      lines.append("# fmt: on")
  
      new_source = "\n".join(lines) + "\n"
  
      if new_source != source:
          learned_dfa_file.write_text(new_source, encoding="utf-8")
  
file_path: catboost/contrib/python/hypothesis/py3/hypothesis/internal/conjecture/shrinking/dfas.py
imports: import hashlib
  import math
  from itertools import islice
  from pathlib import Path
  from hypothesis import HealthCheck, settings
  from hypothesis.errors import HypothesisException
  from hypothesis.internal.conjecture.data import ConjectureResult, Status
  from hypothesis.internal.conjecture.dfa.lstar import LStar
  from hypothesis.internal.conjecture.shrinking.learned_dfas import SHRINKING_DFAS, __file__
label_desc: Use image node_id 1 for calling a global function with example usage: update_learned_dfas() without return types