class_docstr: A fairly basic optimiser designed to increase the value of scores for
  targeted property-based testing.
  
  This implements a fairly naive hill climbing algorithm based on randomly
  regenerating parts of the test case to attempt to improve the result. It is
  not expected to produce amazing results, because it is designed to be run
  in a fairly small testing budget, so it prioritises finding easy wins and
  bailing out quickly if that doesn't work.
  
  For more information about targeted property-based testing, see
  Löscher, Andreas, and Konstantinos Sagonas. "Targeted property-based
  testing." Proceedings of the 26th ACM SIGSOFT International Symposium on
  Software Testing and Analysis. ACM, 2017.
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code: def run(self):
      self.hill_climb()
  
file_path: catboost/contrib/python/hypothesis/py3/hypothesis/internal/conjecture/optimiser.py
imports: from hypothesis.internal.compat import int_from_bytes, int_to_bytes
  from hypothesis.internal.conjecture.data import Status
  from hypothesis.internal.conjecture.engine import BUFFER_SIZE
  from hypothesis.internal.conjecture.junkdrawer import find_integer
  from hypothesis.internal.conjecture.pareto import NO_SCORE
label_desc: Use image node_id 2 for calling the Optimiser obj's underlying member method code with example usage: obj.run() without return types