code: def main(
      bs: Param("Batch size", int) = 104,
      sl: Param("Sequence length", int) = 72,
  ):
      dls, vocab = get_data(bs, sl)
      config = awd_lstm_lm_config.copy()
      config.update(
          {
              "input_p": 0.6,
              "output_p": 0.4,
              "weight_p": 0.5,
              "embed_p": 0.1,
              "hidden_p": 0.2,
          }
      )
      model = get_language_model(AWD_LSTM, len(vocab), config=config)
      opt_func = partial(Adam, wd=0.1, eps=1e-7)
      alpha, beta = (3, 2)
      cbs = [
          MixedPrecision(clip=0.1),
          ModelResetter,
          RNNRegularizer(alpha, beta),
      ]
      learn = Learner(
          model,
          dls,
          loss_func=CrossEntropyLossFlat(),
          opt_func=opt_func,
          cbs=cbs,
          metrics=[accuracy, Perplexity()],
      )
      learn.fit_one_cycle(90, 5e-3, moms=(0.8, 0.7, 0.8), div=10)
  
imports: from fastai.basics import *
  from fastai.text.all import *
  from fastai.callback.all import *
  from fastcore.script import *
label_desc: Use image node_id 4 for calling a global function with example usage: main(bs, sl) without return types