class_docstr: Base class for implementations of deterministic finite
  automata.
  
  This is abstract to allow for the possibility of states
  being calculated lazily as we traverse the DFA (which
  we make heavy use of in our L* implementation - see
  lstar.py for details).
  
  States can be of any hashable type.
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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 _, j in self.raw_transitions(state):
          if j not in seen:
              seen.add(j)
              result.append(j)
      return tuple(result)
  
imports: import threading
  from collections import Counter, defaultdict, deque
  from math import inf
  from hypothesis.internal.reflection import proxies
label_desc: Use image node_id 14 for calling the DFA obj's underlying member method code with example usage: obj.successor_states(state) and returns: tuple