class_docstr: ## Auto regressive model
class_docstr_tok: ["#","#","Auto","regressive","model"]
code: def forward(self, x: torch.Tensor):
      x = self.src_embed(x)
      # Embed the tokens (`src`) and run it through the the transformer
      res, state = self.lstm(x)
      # Generate logits of the next token
      return self.generator(res), state
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/hypernetworks/experiment.py
imports: import torch
  import torch.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.hypernetworks.hyper_lstm import HyperLSTM
  from labml_nn.lstm import LSTM
label_desc: Use image node_id 2 for calling the AutoregressiveModel obj's underlying member method code with example usage: obj.forward(x) and returns: self,  state