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.
class_docstr_tok: ["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","."]
docstr: Iterates over all pairs (byte, state) of transitions
  which do not lead to dead states.
docstr_tok: ["Iterates","over","all","pairs","(","byte",",","state",")","of","transitions","which","do","not","lead","to","dead","states","."]
code: def transitions(self, i):
      """Iterates over all pairs (byte, state) of transitions
      which do not lead to dead states."""
      for c, j in self.raw_transitions(i):
          if not self.is_dead(j):
              yield c, j
  
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 7 for calling the DFA obj's underlying member method code with example usage: obj.transitions(i) without return types