class_docstr: This implements a lazily calculated DFA where states
  are labelled by some string that reaches them, and are
  distinguished by a membership test and a set of experiments.
docstr: Returns all of the distinct states that can be reached via one
  transition from ``state``, in the lexicographic order of the
  smallest character that reaches them.
docstr_tok: ["Returns","all","of","the","distinct","states","that","can","be","reached","via","one","transition","from","``","state","``",",","in","the","lexicographic","order","of","the","smallest","character","that","reaches","them","."]
code: def successor_states(self, state):
      """Returns all of the distinct states that can be reached via one
      transition from ``state``, in the lexicographic order of the
      smallest character that reaches them."""
      seen = set()
      result = []
      for c in self.__lstar.normalizer.representatives():
          j = self.transition(state, c)
          if j not in seen:
              seen.add(j)
              result.append(j)
      return tuple(result)
  
imports: from bisect import bisect_right, insort
  from collections import Counter
  import attr
  from hypothesis.errors import InvalidState
  from hypothesis.internal.conjecture.dfa import DFA, cached
  from hypothesis.internal.conjecture.junkdrawer import IntList, NotFound, SelfOrganisingList, find_integer
label_desc: Use image node_id 7 for calling the LearnedDFA obj's underlying member method code with example usage: obj.successor_states(state) and returns: tuple