code: def compute_perplexity(result):
      """Compute and add the perplexity to the LogReport.
  
      :param dict result: The current observations
      """
      # Routine to rewrite the result dictionary of LogReport to add perplexity values
      result["perplexity"] = np.exp(
          result["main/nll"] / result["main/count"]
      )
      if "validation/main/nll" in result:
          result["val_perplexity"] = np.exp(
              result["validation/main/nll"]
              / result["validation/main/count"]
          )
  
imports: import copy
  import json
  import logging
  import numpy
  import torch
  import torch.nn
  from chainer import Chain, reporter, training
  from chainer.dataset import convert
  from chainer.training import extensions
  from torch.nn.parallel import data_parallel
  from espnet.asr.asr_utils import snapshot_object, torch_load, torch_resume, torch_snapshot
  from espnet.lm.lm_utils import MakeSymlinkToBestModel, ParallelSentenceIterator, count_tokens, load_dataset, read_tokens
  from espnet.nets.lm_interface import LMInterface, dynamic_import_lm
  from espnet.optimizer.factory import dynamic_import_optimizer
  from espnet.scheduler.pytorch import PyTorchScheduler
  from espnet.scheduler.scheduler import dynamic_import_scheduler
  from espnet.utils.deterministic_utils import set_deterministic_pytorch
  from espnet.utils.training.evaluator import BaseEvaluator
  from espnet.utils.training.iterators import ShufflingEnabler
  from espnet.utils.training.tensorboard_logger import TensorboardLogger
  from espnet.utils.training.train_utils import check_early_stop, set_early_stop
label_desc: Use image node_id 1 for calling a global function with example usage: compute_perplexity(result) without return types