code: def awd_lstm_clas_split(model):
      "Split a RNN `model` in groups for differential learning rates."
      groups = [
          nn.Sequential(
              model[0].module.encoder, model[0].module.encoder_dp
          )
      ]
      groups += [
          nn.Sequential(rnn, dp)
          for rnn, dp in zip(
              model[0].module.rnns, model[0].module.hidden_dps
          )
      ]
      groups = L(groups + [model[1]])
      return groups.map(params)
  
imports: from __future__ import annotations
  from ...data.all import *
  from ..core import *
label_desc: Use image node_id 3 for calling a global function with example usage: awd_lstm_clas_split(model) and returns: groups