code: def _model(c: Configs):
      """
      Create the model
      """
  
      # Create hourglass model
      hour_glass = HourGlass(
          c.n_heads, c.d_model, c.dropout, c.d_ff, c.shortening_factors
      )
      # Create the auto-regressive wrapper
      m = AutoregressiveTransformer(
          c.n_tokens, c.d_model, c.dropout, hour_glass
      ).to(c.device)
  
      #
      return m
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/transformers/hour_glass/experiment.py
imports: import math
  from typing import List
  import torch
  from torch import 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.transformers.hour_glass import HourGlass
  from labml_nn.transformers.positional_encoding import PositionalEncoding
label_desc: Use image node_id 1 for calling a global function with example usage: _model(c) and returns: m