code: def allocate_buffers(self):
          "Create the ragged array that will be filled when we ask for items."
          if self.ite_len is None: len(self)
          self.idx   = LanguageModelPreLoader.CircularIndex(len(self.dataset.x.items), not self.backwards)
          self.batch = np.zeros((self.bs, self.bptt+1), dtype=np.int64)
          self.batch_x, self.batch_y = self.batch[:,0:self.bptt], self.batch[:,1:self.bptt+1]
          #ro: index of the text we're at inside our datasets for the various batches
          self.ro    = np.zeros(self.bs, dtype=np.int64)
          #ri: index of the token we're at inside our current text for the various batches
          self.ri    = np.zeros(self.bs, dtype=np.intf allocate_buffers(self):
          "Create the ragged array that will be filled when we ask for items."
          if self.ite_len is None: len(self)
          self.idx   = LanguageModelPreLoader.CircularIndex(len(self.dataset.x.items), not self.backwards)
          self.batch = np.zeros((self.bs, self.bptt+1), dtype=np.int64)
          self.batch_x, self.batch_y = self.batch[:,0:self.bptt], self.batch[:,1:self.bptt+1]
          #ro: index of the text we're at inside our datasets for the various batches
          self.ro    = np.zeros(self.bs, dtype=np.int64)
          #ri: index of the token we're at inside our current text for the various batches
          self.ri    = np.zeros(self.bs, dtype=np.int)
docs: Create the ragged array that will be filled when we ask for items.