code: def autoregressive_model(c: Configs):
      """
      ### Initialize the auto-regressive model
      """
      from labml_nn.transformers.xl import RelativeMultiHeadAttention
      from labml_nn.transformers.feed_forward import FeedForward
  
      m = AutoregressiveModel(
          c.n_tokens,
          c.d_model,
          CompressiveTransformer(
              CompressiveTransformerLayer(
                  d_model=c.d_model,
                  self_attn=RelativeMultiHeadAttention(
                      c.heads, c.d_model, c.dropout
                  ),
                  feed_forward=FeedForward(
                      c.d_model, c.d_ff, c.dropout
                  ),
                  dropout_prob=c.dropout,
                  compress=Conv1dCompression(
                      c.compression_rate, c.d_model
                  ),
              ),
              c.n_layers,
          ),
      )
      return m.to(c.device)
  
imports: from typing import List, Tuple, NamedTuple
  import torch
  import torch.nn
  from labml import experiment, tracker, monit, logger
  from labml.configs import option
  from labml.logger import Text
  from labml_helpers.metrics.simple_state import SimpleStateModule
  from labml_helpers.module import Module
  from labml_helpers.train_valid import BatchIndex, hook_model_outputs
  from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
  from labml_nn.transformers.compressive import CompressiveTransformer, AttentionReconstructionLoss, CompressiveTransformerLayer, Conv1dCompression
label_desc: Use image node_id 1 for calling a global function with example usage: autoregressive_model(c) and returns: m