class_docstr: Trainer for GAN-based training.
  
  If you'd like to use this trainer, the model must inherit
  espnet.train.abs_gan_espnet_model.AbsGANESPnetModel.
docstr: Validate one epoch.
docstr_tok: ["Validate","one","epoch","."]
code: def validate_one_epoch(
      cls,
      model: torch.nn.Module,
      iterator: Iterable[Dict[str, torch.Tensor]],
      reporter: SubReporter,
      options: GANTrainerOptions,
      distributed_option: DistributedOption,
  ) -> None:
      """Validate one epoch."""
      assert check_argument_types()
      ngpu = options.ngpu
      no_forward_run = options.no_forward_run
      distributed = distributed_option.distributed
      generator_first = options.generator_first
  
      model.eval()
  
      # [For distributed] Because iteration counts are not always equals between
      # processes, send stop-flag to the other processes if iterator is finished
      iterator_stop = torch.tensor(0).to("cuda" if ngpu > 0 else "cpu")
      for _, batch in iterator:
          assert isinstance(batch, dict), type(batch)
          if distributed:
              torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
              if iterator_stop > 0:
                  break
  
          batch = to_device(batch, "cuda" if ngpu > 0 else "cpu")
          if no_forward_run:
              continue
  
          if generator_first:
              turns = ["generator", "discriminator"]
          else:
              turns = ["discriminator", "generator"]
          for turn in turns:
              retval = model(
                  forward_generator=turn == "generator", **batch
              )
              if isinstance(retval, dict):
                  stats = retval["stats"]
                  weight = retval["weight"]
              else:
                  _, stats, weight = retval
              if ngpu > 1 or distributed:
                  # Apply weighted averaging for stats.
                  # if distributed, this method can also apply all_reduce()
                  stats, weight = recursive_average(
                      stats, weight, distributed
                  )
              reporter.register(stats, weight)
  
          reporter.next()
  
      else:
          if distributed:
              iterator_stop.fill_(1)
              torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
  
imports: import argparse
  import dataclasses
  import logging
  import time
  from contextlib import contextmanager
  from typing import Dict, Iterable, List, Optional, Sequence, Tuple
  import torch
  from packaging.version import parse
  from typeguard import check_argument_types
  from espnet2.schedulers.abs_scheduler import AbsBatchStepScheduler, AbsScheduler
  from espnet2.torch_utils.device_funcs import to_device
  from espnet2.torch_utils.recursive_op import recursive_average
  from espnet2.train.distributed_utils import DistributedOption
  from espnet2.train.reporter import SubReporter
  from espnet2.train.trainer import Trainer, TrainerOptions
  from espnet2.utils.build_dataclass import build_dataclass
  from espnet2.utils.types import str2bool
label_desc: Use image node_id 4 for calling the GANTrainer obj's underlying member method code with example usage: obj.validate_one_epoch(cls, model, iterator, reporter, options, distributed_option) without return types