- if self.training and self.proba_flashback:
- insert_flash_back(
- self.rec_V,
- V,
- self.rec_K,
- K,
- t0,
- t1,
- CL,
- proba=self.proba_flashback / CL,
- )
+ if self.training and self.proba_flashback > 0.0:
+ # insert_flash_back(self.rec_V,V,self.rec_K,K,t0,t1,CL,proba=self.proba_flashback / CL,)
+
+ # This piece of code makes the assumption that there is
+ # nothing informative before t0, otherwise we'd have to
+ # implement a cache for V and K too. This should not be
+ # too much of a problem since this is used only during
+ # train, where full sequence are available