code: def _model(c: Configs):
      """
      #### Initialize the model
      """
  
      # Create FTA activation module
      fta = FTA(
          c.fta_lower_limit, c.fta_upper_limit, c.fta_delta, c.fta_eta
      )
      # Create the transformer.
      # We re-use [`TransformerLayer`](../../transformers/models.html#TransformerLayer) and
      # [`MultiHeadAttention`](../../transformers/mha.html) implementations.
      m = AutoregressiveTransformer(
          c.n_tokens,
          c.d_model,
          c.n_layers,
          TransformerLayer(
              d_model=c.d_model,
              feed_forward=FeedForwardFTA(
                  d_model=c.d_model,
                  d_ff=c.d_ff,
                  activation=fta,
                  dropout=0.1,
              ),
              self_attn=MultiHeadAttention(
                  c.n_heads, c.d_model, dropout_prob=0.0
              ),
              dropout_prob=0.0,
          ),
      )
  
      # Move to the device
      return m.to(c.device)
  
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.activations.fta import FTA
  from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
  from labml_nn.transformers import MultiHeadAttention, TransformerLayer
  from labml_nn.transformers.utils import subsequent_mask
label_desc: Use image node_id 1 for calling a global function with example usage: _model(c) and returns: m