class_docstr: Object that holds dropout-related functionality for RNN cells.
  
  This class is not a standalone RNN cell. It suppose to be used with a RNN
  cell by multiple inheritance. Any cell that mix with class should have
  following fields:
  
  - `dropout`: a float number in the range `[0, 1]`.
      Dropout rate for the input tensor.
  - `recurrent_dropout`: a float number in the range `[0, 1]`.
      Dropout rate for the recurrent connections.
  - `seed_generator`, an instance of `backend.random.SeedGenerator`.
  
  This object will create and cache dropout masks, and reuse them for
  all incoming steps, so that the same mask is used for every step.
docstr: Reset the cached dropout mask if any.
  
  The RNN layer invokes this in the `call()` method
  so that the cached mask is cleared after calling `cell.call()`. The
  mask should be cached across all timestep within the same batch, but
  shouldn't be cached between batches.
docstr_tok: ["Reset","the","cached","dropout","mask","if","any",".","The","RNN","layer","invokes","this","in","the","`","call","(",")","`","method","so","that","the","cached","mask","is","cleared","after","calling","`","cell.call","(",")","`",".","The","mask","should","be","cached","across","all","timestep","within","the","same","batch",",","but","should","n't","be","cached","between","batches","."]
code: def reset_dropout_mask(self):
      """Reset the cached dropout mask if any.
  
      The RNN layer invokes this in the `call()` method
      so that the cached mask is cleared after calling `cell.call()`. The
      mask should be cached across all timestep within the same batch, but
      shouldn't be cached between batches.
      """
      self._dropout_mask = None
  
imports: from keras import backend
  from keras import ops
label_desc: Use image node_id 3 for calling the DropoutRNNCell obj's underlying member method code with example usage: obj.reset_dropout_mask() without return types