code: def decode(
      input_ids,
      model,
      max_length,
      top_k=1,
      top_p=0.0,
      temperature=1.0,
      eos_token_id=None,
      teacher_outputs=None,
      vocab_size=None,
      tensor_parallel=1,
      cg=False,
      enable_timing=False,
  ):
      """Decoding, either greedy or with top-k or top-p sampling.
      If top-k = 0, don't limit the number of candidates (pure sampling).
      Top-k and top-p can be used together. If top_k > 0 and top_p > 0, then top-k is applied first,
      then top-p.
      We assume that all sequences in the same batch have the same length.
  
      Arguments:
          input_ids: (batch, seq_len)
          max_length: int
          teacher_outputs (optional): (batch, seq_len). If provided, instead of sampling from the
              logits, the next token is taken from the teacher_outputs. Useful for testing.
      Returns: GreedySearchDecoderOnlyOutput or SampleDecoderOnlyOutput, with the following fields:
          sequences: (batch, max_length)
          scores: tuples of (batch, vocab_size)
      """
      batch_size, seqlen_og = input_ids.shape
      teacher_output_len = (
          teacher_outputs.shape[1] if teacher_outputs is not None else 0
      )
      if cg:
          if not hasattr(model, "_decoding_cache"):
              model._decoding_cache = None
          model._decoding_cache = update_graph_cache(
              model,
              model._decoding_cache,
              batch_size,
              seqlen_og,
              max_length,
              tensor_parallel=tensor_parallel,
          )
          inference_params = model._decoding_cache.inference_params
          inference_params.reset(max_length, batch_size)
      else:
          inference_params = InferenceParams(
              max_seqlen=max_length, max_batch_size=batch_size
          )
  
      def get_logits(input_ids, inference_params):
          decoding = inference_params.seqlen_offset > 0
          if decoding:
              position_ids = torch.full(
                  (batch_size, 1),
                  inference_params.seqlen_offset,
                  dtype=torch.long,
                  device=input_ids.device,
              )
          else:
              position_ids = None
          if not cg or not decoding:
              logits = model(
                  input_ids,
                  position_ids=position_ids,
                  inference_params=inference_params,
                  num_last_tokens=1,
              ).logits.squeeze(dim=1)
          else:
              logits = model._decoding_cache.run(
                  input_ids,
                  position_ids,
                  inference_params.seqlen_offset,
              ).squeeze(dim=1)
          return (
              logits[..., :vocab_size]
              if vocab_size is not None
              else logits
          )
  
      def sample_tokens(logits, inference_params):
          if (
              teacher_outputs is None
              or teacher_output_len <= inference_params.seqlen_offset
          ):
              token = sample(
                  logits,
                  top_k=top_k,
                  top_p=top_p,
                  temperature=temperature,
              )
          else:
              token = teacher_outputs[:, inference_params.seqlen_offset]
          # return rearrange(token, "b -> b 1")
          return token.unsqueeze(1)
  
      def should_stop(current_token, inference_params):
          if inference_params.seqlen_offset == 0:
              return False
          if (
              eos_token_id is not None
              and (current_token == eos_token_id).all()
          ):
              return True
          if inference_params.seqlen_offset >= max_length - 1:
              return True
          return False
  
      start = torch.cuda.Event(enable_timing=enable_timing)
      end = torch.cuda.Event(enable_timing=enable_timing)
  
      if enable_timing:
          if tensor_parallel > 1:
              torch.distributed.barrier()
          start.record()
      scores, sequences = [], [input_ids]
      while not should_stop(sequences[-1], inference_params):
          scores.append(get_logits(sequences[-1], inference_params))
          inference_params.seqlen_offset += sequences[-1].shape[1]
          sequences.append(sample_tokens(scores[-1], inference_params))
      if enable_timing:
          end.record()
          if tensor_parallel > 1:
              torch.distributed.barrier()
          torch.cuda.synchronize()
          print(
              f"Prompt processing + decoding time: {(start.elapsed_time(end)):.0f}ms"
          )
      output_cls = (
          GreedySearchDecoderOnlyOutput
          if top_k == 1
          else SampleDecoderOnlyOutput
      )
      return output_cls(
          sequences=torch.cat(sequences, dim=1), scores=tuple(scores)
      )
  
imports: import gc
  import time
  from collections import namedtuple
  from dataclasses import dataclass, field
  from functools import partial
  from typing import Callable, Optional, Sequence, Union
  import torch
  import torch.nn.functional
  from einops import rearrange, repeat
  from torch import Tensor
  from torch.profiler import ProfilerActivity, profile, record_function
  from transformers.generation import GreedySearchDecoderOnlyOutput, SampleDecoderOnlyOutput
label_desc: Use image node_id 4 for calling a global function with example usage: decode(input_ids, model, max_length, top_k, top_p, temperature, eos_token_id, teacher_outputs, vocab_size, tensor_parallel, cg, enable_timing) and returns: output_cls, unknown, token, False, False, True, True