class_docstr: Save the raw and dropped-out outputs and only keep the true output for loss computation
class_docstr_tok: ["Save","the","raw","and","dropped-out","outputs","and","only","keep","the","true","output","for","loss","computation"]
code: def after_pred(self):
      self.learn.pred, self.raw_out, self.out = [
          o[-1] if is_listy(o) else o for o in self.pred
      ]
  
file_path: fastai/fastai/callback/rnn.py
imports: from __future__ import annotations
  from ..basics import *
label_desc: Use image node_id 1 for calling the RNNCallback obj's underlying member method code with example usage: obj.after_pred() without return types