class_docstr: ## Auto-Regressive model
  
  This is a autoregressive transformer model that uses DeepNorm.
class_docstr_tok: ["#","#","Auto-Regressive","model","This","is","a","autoregressive","transformer","model","that","uses","DeepNorm","."]
docstr: :param x: are the input tokens of shape `[seq_len, batch_size]`
docstr_tok: [":","param","x",":","are","the","input","tokens","of","shape","`","[","seq_len",",","batch_size","]","`"]
code: def forward(self, x: torch.Tensor):
      """
      :param x: are the input tokens of shape `[seq_len, batch_size]`
      """
      # Get the token embeddings
      x = self.emb(x)
      # Transformer encoder
      x = self.transformer(x)
      # Get logits
      x = self.readout(x)
  
      # Return results
      return x, None
  
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 2 for calling the AutoregressiveTransformer obj's underlying member method code with example usage: obj.forward(x) and returns: x,  None