class_docstr: Trainer having a optimizer.
  
  If you'd like to use multiple optimizers, then inherit this class
  and override the methods if necessary - at least "train_one_epoch()"
  
  >>> class TwoOptimizerTrainer(Trainer):
  ...     @classmethod
  ...     def add_arguments(cls, parser):
  ...         ...
  ...
  ...     @classmethod
  ...     def train_one_epoch(cls, model, optimizers, ...):
  ...         loss1 = model.model1(...)
  ...         loss1.backward()
  ...         optimizers[0].step()
  ...
  ...         loss2 = model.model2(...)
  ...         loss2.backward()
  ...         optimizers[1].step()
code: def validate_one_epoch(
      cls,
      model: torch.nn.Module,
      iterator: Iterable[Dict[str, torch.Tensor]],
      reporter: SubReporter,
      options: TrainerOptions,
      distributed_option: DistributedOption,
  ) -> None:
      assert check_argument_types()
      ngpu = options.ngpu
      no_forward_run = options.no_forward_run
      distributed = distributed_option.distributed
  
      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 utt_id, 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["utt_id"] = utt_id
  
          batch = to_device(batch, "cuda" if ngpu > 0 else "cpu")
          if no_forward_run:
              continue
  
          retval = model(**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 dataclasses import is_dataclass
  from pathlib import Path
  from typing import Dict, Iterable, List, Optional, Sequence, Tuple, Union
  import humanfriendly
  import numpy
  import torch
  import torch.nn
  import torch.optim
  from packaging.version import parse
  from typeguard import check_argument_types
  from espnet2.iterators.abs_iter_factory import AbsIterFactory
  from espnet2.main_funcs.average_nbest_models import average_nbest_models
  from espnet2.main_funcs.calculate_all_attentions import calculate_all_attentions
  from espnet2.schedulers.abs_scheduler import AbsBatchStepScheduler, AbsEpochStepScheduler, AbsScheduler, AbsValEpochStepScheduler
  from espnet2.torch_utils.add_gradient_noise import add_gradient_noise
  from espnet2.torch_utils.device_funcs import to_device
  from espnet2.torch_utils.recursive_op import recursive_average
  from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
  from espnet2.train.abs_espnet_model import AbsESPnetModel
  from espnet2.train.distributed_utils import DistributedOption
  from espnet2.train.reporter import Reporter, SubReporter
  from espnet2.utils.build_dataclass import build_dataclass
  from espnet2.utils.kwargs2args import kwargs2args
label_desc: Use image node_id 7 for calling the Trainer obj's underlying member method code with example usage: obj.validate_one_epoch(cls, model, iterator, reporter, options, distributed_option) without return types