parameters: (self, game, models, truncate_negative=False)
docstring: Creates a new `_RcfrSolver`.
  
      Args:
        game: An OpenSpiel `Game`.
        models: Current policy models (optimizable array-like -> `torch.Tensor`
          callables) for both players.
        truncate_negative: Whether or not to truncate negative (approximate)
          cumulative regrets to zero to implement RCFR+. Defaults to `False`.
docstring_summary: Creates a new `_RcfrSolver`.
docstring_tokens: Creates a new _RcfrSolver .
function: def __init__(self, game, models, truncate_negative=False):
      """Creates a new `_RcfrSolver`.
  
      Args:
        game: An OpenSpiel `Game`.
        models: Current policy models (optimizable array-like -> `torch.Tensor`
          callables) for both players.
        truncate_negative: Whether or not to truncate negative (approximate)
          cumulative regrets to zero to implement RCFR+. Defaults to `False`.
      """
      self._game = game
      self._models = models
      self._truncate_negative = truncate_negative
      self._root_wrapper = RootStateWrapper(game.new_initial_state())
  
      self._cumulative_seq_probs = [
          np.zeros(n) for n in self._root_wrapper.num_player_sequences
      ]
function_tokens: def __init__ ( self , game , models , truncate_negative = False ) : self . _game = game self . _models = models self . _truncate_negative = truncate_negative self . _root_wrapper = RootStateWrapper ( game . new_initial_state ( ) ) self . _cumulative_seq_probs = [ np . zeros ( n ) for n in self . _root_wrapper . num_player_sequences ]