original_docstring: 
          Simulate a random game except that we play all known critical
          cells first, return the winning player and record critical cells at the end.
  
          
docstring: Simulate a random game except that we play all known critical
  cells first, return the winning player and record critical cells at the end.
docstring_tokens: Simulate a random game except that we play all known critical cells first return the winning player and record critical cells at the end .
code: def roll_out(self, state: GameState) -> tuple:
          """
          Simulate a random game except that we play all known critical
          cells first, return the winning player and record critical cells at the end.
  
          """
          moves = state.moves()
          first = state.turn()
          if first == GameMeta.PLAYERS["black"]:
              current_reply = self.black_reply
              other_reply = self.white_reply
          else:
              current_reply = self.white_reply
              other_reply = self.black_reply
          black_moves = []
          white_moves = []
          last_move = None
          while state.winner == GameMeta.PLAYERS["none"]:
              if last_move in current_reply:
                  move = current_reply[last_move]
                  if move not in moves or random() > MCTSMeta.RANDOMNESS:
                      move = choice(moves)
              else:
                  move = choice(moves)
              if state.turn() == GameMeta.PLAYERS["black"]:
                  black_moves.append(move)
              else:
                  white_moves.append(move)
              current_reply, other_reply = other_reply, current_reply
              state.play(move)
              moves.remove(move)
              last_move = move
  
          black_rave_pts = []
          white_rave_pts = []
  
          for x in range(state.size):
              for y in range(state.size):
                  if state.board[(x, y)] == GameMeta.PLAYERS["black"]:
                      black_rave_pts.append((x, y))
                  elif state.board[(x, y)] == GameMeta.PLAYERS["white"]:
                      white_rave_pts.append((x, y))
  
          # This part of the algorithm probably deals with adjusting
          # the indices of the arrays.
  
          offset = 0
          skip = 0
          if state.winner == GameMeta.PLAYERS["black"]:
  
              if first == GameMeta.PLAYERS["black"]:
                  offset = 1
              if state.turn() == GameMeta.PLAYERS["black"]:
                  skip = 1
              for i in range(len(white_moves) - skip):
                  self.black_reply[white_moves[i]] = black_moves[i + offset]
          else:
              if first == GameMeta.PLAYERS["white"]:
                  offset = 1
              if state.turn() == GameMeta.PLAYERS["white"]:
                  skip = 1
              for i in range(len(black_moves) - skip):
                  self.white_reply[black_moves[i]] = white_moves[i + offset]
  
          return state.winner, black_rave_pts, white_rave_pts
code_tokens: def roll_out ( self , state : GameState ) -> tuple : moves = state . moves ( ) first = state . turn ( ) if first == GameMeta . PLAYERS [ "black" ] : current_reply = self . black_reply other_reply = self . white_reply else : current_reply = self . white_reply other_reply = self . black_reply black_moves = [ ] white_moves = [ ] last_move = None while state . winner == GameMeta . PLAYERS [ "none" ] : if last_move in current_reply : move = current_reply [ last_move ] if move not in moves or random ( ) > MCTSMeta . RANDOMNESS : move = choice ( moves ) else : move = choice ( moves ) if state . turn ( ) == GameMeta . PLAYERS [ "black" ] : black_moves . append ( move ) else : white_moves . append ( move ) current_reply , other_reply = other_reply , current_reply state . play ( move ) moves . remove ( move ) last_move = move black_rave_pts = [ ] white_rave_pts = [ ] for x in range ( state . size ) : for y in range ( state . size ) : if state . board [ ( x , y ) ] == GameMeta . PLAYERS [ "black" ] : black_rave_pts . append ( ( x , y ) ) elif state . board [ ( x , y ) ] == GameMeta . PLAYERS [ "white" ] : white_rave_pts . append ( ( x , y ) ) offset = 0 skip = 0 if state . winner == GameMeta . PLAYERS [ "black" ] : if first == GameMeta . PLAYERS [ "black" ] : offset = 1 if state . turn ( ) == GameMeta . PLAYERS [ "black" ] : skip = 1 for i in range ( len ( white_moves ) - skip ) : self . black_reply [ white_moves [ i ] ] = black_moves [ i + offset ] else : if first == GameMeta . PLAYERS [ "white" ] : offset = 1 if state . turn ( ) == GameMeta . PLAYERS [ "white" ] : skip = 1 for i in range ( len ( black_moves ) - skip ) : self . white_reply [ black_moves [ i ] ] = white_moves [ i + offset ] return state . winner , black_rave_pts , white_rave_pts
short_docstring: Simulate a random game except that we play all known critical
  cells first, return the winning player and record critical cells at the end.
short_docstring_tokens: Simulate a random game except that we play all known critical cells first return the winning player and record critical cells at the end .
comment: """
          Simulate a random game except that we play all known critical
          cells first, return the winning player and record critical cells at the end.
  
          """$SEP$# This part of the algorithm probably deals with adjusting$SEP$# the indices of the arrays.