code: def gen_single_token(self, constraints=None, mask=None):
      self.end_beam_search()
  
      # Simple sampling case:
  
      if self.sequence is not None:
          logits = self.model.forward(
              self.sequence[:, -1:],
              self.cache,
              lora=self.lora,
              input_mask=mask,
          )
          self.apply_rep_penalty(logits)
  
          logits[:, :, self.tokenizer.bos_token_id] = -10000.0
  
          if constraints is not None:
              for c in constraints:
                  logits[:, :, c] += 10000.0
              logits[:, :, :] -= 10000.0
  
          token, _ = self.batched_sample(
              logits,
              self.settings.temperature,
              self.settings.top_k,
              self.settings.top_p,
              self.settings.min_p + 0.01
              if constraints is not None
              else 0.0,
              self.settings.typical,
          )
  
      else:
          # bos = torch.Tensor([[self.tokenizer.bos_token_id]]).long()
          # logits = self.model.forward(bos, self.cache)
          # self.cache.current_seq_len = 0
  
          if constraints is not None:
              token = constraints[0]
          else:
              token = torch.Tensor(
                  [[self.tokenizer.bos_token_id]]
              ).long()
  
      self.gen_accept_token(token)
      return token
  
imports: import cuda_ext
  from model import ExLlama, ExLlamaCache
  from lora import ExLlamaLora
  import torch
  import torch.nn.functional
label_desc: Use image node_id 20 for calling the ExLlamaGenerator obj's underlying member method code with example usage: obj.gen_single_token(constraints, mask) and returns: token