code: def feedback_transformer(c: Configs):
      """
      Create [original feedback transformer](index.html).
      """
      from labml_nn.transformers.feedback import (
          FeedbackTransformer,
          FeedbackTransformerLayer,
          FeedbackAttention,
          FeedForward,
      )
  
      return AutoregressiveModel(
          c.n_tokens,
          c.d_model,
          FeedbackTransformer(
              FeedbackTransformerLayer(
                  d_model=c.d_model,
                  attn=FeedbackAttention(c.heads, c.d_model, c.dropout),
                  feed_forward=FeedForward(
                      c.d_model, c.d_ff, c.dropout
                  ),
                  dropout_prob=c.dropout,
              ),
              c.n_layers,
          ),
      ).to(c.device)
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/transformers/feedback/experiment.py
imports: import torch
  from torch import nn
  from labml import experiment
  from labml.configs import option
  from labml.utils.pytorch import get_modules
  from labml_helpers.module import Module
  from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
  from labml_nn.transformers import Encoder, Generator, TransformerConfigs
  from labml_nn.transformers.utils import subsequent_mask
label_desc: Use image node_id 1 for calling a global function with example usage: feedback_transformer(c) and returns: AutoregressiveModel