code: def _model(c: Configs):
      """
      #### Initialize the model
      """
      m = AutoregressiveTransformer(
          c.n_tokens,
          c.d_model,
          c.n_layers,
          DeepNormTransformerLayer(
              d_model=c.d_model,
              deep_norm_alpha=c.deep_norm_alpha,
              deep_norm_beta=c.deep_norm_beta,
              feed_forward=FeedForward(
                  d_model=c.d_model, d_ff=c.d_model * 4
              ),
              self_attn=MultiHeadAttention(
                  c.n_heads, c.d_model, dropout_prob=0.0
              ),
          ),
      )
  
      return m.to(c.device)
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/normalization/deep_norm/experiment.py
imports: import copy
  import torch
  import torch.nn
  from labml import experiment
  from labml.configs import option
  from labml_helpers.module import Module
  from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
  from labml_nn.normalization.deep_norm import DeepNormTransformerLayer
  from labml_nn.transformers import MultiHeadAttention
  from labml_nn.transformers.feed_forward import FeedForward
label_desc: Use image node_id 3 for calling a global function with example usage: _model(c) and returns: m