code: def start(self, start_with_nothing):
      state = LMState()
      prefix = torch.LongTensor([[self.dictionary.eos()]])
      incremental_state = {} if self.save_incremental else None
      with torch.no_grad():
          res = self.model(
              prefix.cuda(), incremental_state=incremental_state
          )
          probs = self.model.get_normalized_probs(
              res, log_probs=True, sample=None
          )
  
      if incremental_state is not None:
          incremental_state = apply_to_sample(
              lambda x: x.cpu(), incremental_state
          )
      self.states[state] = FairseqLMState(
          prefix.numpy(), incremental_state, probs[0, -1].cpu().numpy()
      )
      self.stateq.append(state)
  
      return state
  
imports: import gc
  import itertools
  import os.path
  from typing import List
  import warnings
  from collections import deque, namedtuple
  import numpy
  import torch
  from examples.speech_recognition.data.replabels import unpack_replabels
  from fairseq import tasks
  from fairseq.utils import apply_to_sample
  from omegaconf import open_dict
  from fairseq.dataclass.utils import convert_namespace_to_omegaconf
label_desc: Use image node_id 2 for calling the FairseqLM obj's underlying member method code with example usage: obj.start(start_with_nothing) and returns: state