code: def generate_token(
      self, batch: CausalLMBatch
  ) -> Tuple[
      List[Generation], Optional[CausalLMBatch], Tuple[int, int]
  ]:
      start = time.time_ns()
      # slice the attention mask to the correct shape
      attention_mask = batch.attention_mask[
          :, : -batch.padding_right_offset
      ]
  
      logits, past = self.forward(
          batch.input_ids,
          attention_mask,
          batch.position_ids,
          batch.past_key_values,
      )
  
      # Results
      generations: List[Generation] = []
      stopped = True
  
      batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
          batch.top_n_tokens,
          batch.top_n_tokens_tensor,
          torch.log_softmax(logits[:, -1], -1),
      )
  
      start_decode = time.time_ns()
  
      # Zipped iterator
      iterator = zip(
          batch.requests,
          batch.input_lengths,
          batch.prefix_offsets,
          batch.read_offsets,
          logits,
          batch.next_token_choosers,
          batch.stopping_criterias,
          batch.all_input_ids,
          batch.top_n_tokens,
          batch_top_token_ids,
          batch_top_token_logprobs,
      )
  
      # For each member of the batch
      for i, (
          request,
          input_length,
          prefix_offset,
          read_offset,
          logits,
          next_token_chooser,
          stopping_criteria,
          all_input_ids,
          top_n_tokens,
          top_token_ids,
          top_token_logprobs,
      ) in enumerate(iterator):
          # Select next token
          next_token_id, logprobs = next_token_chooser(
              all_input_ids.view(1, -1), logits[-1:, :]
          )
  
          # Append next token to all tokens
          all_input_ids = torch.cat([all_input_ids, next_token_id])
          new_input_length = input_length + 1
  
          # Generated token
          next_token_logprob = logprobs[-1, next_token_id]
          next_token_id_squeezed = next_token_id.squeeze()
          (
              next_token_text,
              prefix_offset,
              read_offset,
          ) = self.decode_token(
              all_input_ids[:, 0], prefix_offset, read_offset
          )
  
          # Evaluate stopping criteria
          stop, reason = stopping_criteria(
              next_token_id_squeezed,
              next_token_text,
          )
  
          if not stop:
              stopped = False
  
          # Shard generations
          # All generations will be appended in the rust sharded client
          if i % self.world_size == self.rank:
              if stop:
                  # Decode generated tokens
                  output_text, _, _ = self.decode_token(
                      all_input_ids[:, 0],
                      prefix_offset=len(all_input_ids)
                      - stopping_criteria.current_tokens
                      - 1,
                      read_offset=len(all_input_ids)
                      - stopping_criteria.current_tokens,
                      skip_special_tokens=True,
                  )
                  # Get seed
                  if isinstance(next_token_chooser.choice, Sampling):
                      seed = next_token_chooser.choice.seed
                  else:
                      seed = None
  
                  generated_text = GeneratedText(
                      output_text,
                      stopping_criteria.current_tokens,
                      reason,
                      seed,
                  )
              else:
                  generated_text = None
  
              # Prefill
              if (
                  stopping_criteria.current_tokens == 1
                  and request.prefill_logprobs
              ):
                  # Remove generated token to only have prefill and add nan for first prompt token
                  prefill_logprobs = [float("nan")] + torch.log_softmax(
                      logits, -1
                  ).gather(1, all_input_ids[1:]).squeeze(1)[
                      -new_input_length:-1
                  ].tolist()
                  prefill_token_ids = all_input_ids[
                      -new_input_length:-1
                  ]
                  prefill_texts = self.tokenizer.batch_decode(
                      prefill_token_ids,
                      clean_up_tokenization_spaces=False,
                      skip_special_tokens=False,
                  )
                  prefill_tokens = Tokens(
                      prefill_token_ids,
                      prefill_logprobs,
                      prefill_texts,
                      is_special=[],
                  )
              else:
                  prefill_tokens = None
  
              if top_n_tokens > 0:
                  toptoken_texts = self.tokenizer.batch_decode(
                      top_token_ids,
                      clean_up_tokenization_spaces=False,
                      skip_special_tokens=False,
                  )
                  special_toptokens = [
                      token_id in self.all_special_ids
                      for token_id in top_token_ids
                  ]
                  top_tokens = Tokens(
                      top_token_ids,
                      top_token_logprobs,
                      toptoken_texts,
                      special_toptokens,
                  )
              else:
                  top_tokens = None
  
              generation = Generation(
                  request.id,
                  prefill_tokens,
                  Tokens(
                      [next_token_id_squeezed],
                      [next_token_logprob],
                      [next_token_text],
                      [
                          next_token_id_squeezed.item()
                          in self.all_special_ids
                      ],
                  ),
                  generated_text,
                  top_tokens,
              )
  
              generations.append(generation)
  
          # Update values
          batch.input_ids[i, 0] = next_token_id
          batch.all_input_ids[i] = all_input_ids
          batch.input_lengths[i] = new_input_length
          batch.prefix_offsets[i] = prefix_offset
          batch.read_offsets[i] = read_offset
          batch.max_input_length = max(
              batch.max_input_length, new_input_length
          )
  
      # We finished all generations in the batch; there is no next batch
      if stopped:
          forward_ns = start_decode - start
          decode_ns = time.time_ns() - start_decode
          return generations, None, (forward_ns, decode_ns)
  
      # Slice unused values from prefill
      batch.input_ids = batch.input_ids[:, :1]
  
      # Update attention_mask as we added a new token to input_ids
      batch.attention_mask[:, -batch.padding_right_offset] = 1
      # Decrease right offset
      batch.padding_right_offset -= 1
  
      # Update position_ids
      batch.position_ids = batch.position_ids[:, -1:] + 1
  
      # Update past key values
      batch.past_key_values = past
  
      forward_ns = start_decode - start
      decode_ns = time.time_ns() - start_decode
      return generations, batch, (forward_ns, decode_ns)
  
imports: import torch
  import time
  from dataclasses import dataclass
  from opentelemetry import trace
  from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase
  from typing import Optional, Tuple, List, Type, Dict
  from text_generation_server.models import Model
  from text_generation_server.utils.tokens import batch_top_tokens
  from text_generation_server.models.types import Batch, Tokens, Generation, GeneratedText
  from text_generation_server.pb import generate_pb2
  from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
label_desc: Use image node_id 5 for calling the CausalLM obj's underlying member method code with example usage: obj.generate_token(batch) and returns: generations,  batch,  forward_ns,  decode_ns, generations,  None,  forward_ns,  decode_ns