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: Train one epoch.
docstr_tok: ["Train","one","epoch","."]
code: def train_one_epoch(
      cls,
      model: torch.nn.Module,
      iterator: Iterable[Tuple[List[str], Dict[str, torch.Tensor]]],
      optimizers: Sequence[torch.optim.Optimizer],
      schedulers: Sequence[Optional[AbsScheduler]],
      scaler: Optional[GradScaler],
      reporter: SubReporter,
      summary_writer,
      options: GANTrainerOptions,
      distributed_option: DistributedOption,
  ) -> bool:
      """Train one epoch."""
      assert check_argument_types()
  
      grad_noise = options.grad_noise
      accum_grad = options.accum_grad
      grad_clip = options.grad_clip
      grad_clip_type = options.grad_clip_type
      log_interval = options.log_interval
      no_forward_run = options.no_forward_run
      ngpu = options.ngpu
      use_wandb = options.use_wandb
      generator_first = options.generator_first
      distributed = distributed_option.distributed
  
      # Check unavailable options
      # TODO(kan-bayashi): Support the use of these options
      if accum_grad > 1:
          raise NotImplementedError(
              "accum_grad > 1 is not supported in GAN-based training."
          )
      if grad_noise:
          raise NotImplementedError(
              "grad_noise is not supported in GAN-based training."
          )
  
      if log_interval is None:
          try:
              log_interval = max(len(iterator) // 20, 10)
          except TypeError:
              log_interval = 100
  
      model.train()
      all_steps_are_invalid = True
      # [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")
  
      start_time = time.perf_counter()
      for iiter, (_, batch) in enumerate(
          reporter.measure_iter_time(iterator, "iter_time"), 1
      ):
          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:
              all_steps_are_invalid = False
              continue
  
          turn_start_time = time.perf_counter()
          if generator_first:
              turns = ["generator", "discriminator"]
          else:
              turns = ["discriminator", "generator"]
          for turn in turns:
              with autocast(scaler is not None):
                  with reporter.measure_time(f"{turn}_forward_time"):
                      retval = model(
                          forward_generator=turn == "generator", **batch
                      )
  
                      # Note(kamo):
                      # Supporting two patterns for the returned value from the model
                      #   a. dict type
                      if isinstance(retval, dict):
                          loss = retval["loss"]
                          stats = retval["stats"]
                          weight = retval["weight"]
                          optim_idx = retval.get("optim_idx")
                          if optim_idx is not None and not isinstance(
                              optim_idx, int
                          ):
                              if not isinstance(
                                  optim_idx, torch.Tensor
                              ):
                                  raise RuntimeError(
                                      "optim_idx must be int or 1dim torch.Tensor, "
                                      f"but got {type(optim_idx)}"
                                  )
                              if optim_idx.dim() >= 2:
                                  raise RuntimeError(
                                      "optim_idx must be int or 1dim torch.Tensor, "
                                      f"but got {optim_idx.dim()}dim tensor"
                                  )
                              if optim_idx.dim() == 1:
                                  for v in optim_idx:
                                      if v != optim_idx[0]:
                                          raise RuntimeError(
                                              "optim_idx must be 1dim tensor "
                                              "having same values for all entries"
                                          )
                                  optim_idx = optim_idx[0].item()
                              else:
                                  optim_idx = optim_idx.item()
  
                      # b. tuple or list type
                      else:
                          raise RuntimeError(
                              "model output must be dict."
                          )
  
                  stats = {
                      k: v for k, v in stats.items() if v is not None
                  }
                  if ngpu > 1 or distributed:
                      # Apply weighted averaging for loss and stats
                      loss = (loss * weight.type(loss.dtype)).sum()
  
                      # if distributed, this method can also apply all_reduce()
                      stats, weight = recursive_average(
                          stats, weight, distributed
                      )
  
                      # Now weight is summation over all workers
                      loss /= weight
  
                  if distributed:
                      # NOTE(kamo): Multiply world_size since DistributedDataParallel
                      # automatically normalizes the gradient by world_size.
                      loss *= torch.distributed.get_world_size()
  
              reporter.register(stats, weight)
  
              with reporter.measure_time(f"{turn}_backward_time"):
                  if scaler is not None:
                      # Scales loss.  Calls backward() on scaled loss
                      # to create scaled gradients.
                      # Backward passes under autocast are not recommended.
                      # Backward ops run in the same dtype autocast chose
                      # for corresponding forward ops.
                      scaler.scale(loss).backward()
                  else:
                      loss.backward()
  
              if scaler is not None:
                  # Unscales the gradients of optimizer's assigned params in-place
                  for iopt, optimizer in enumerate(optimizers):
                      if optim_idx is not None and iopt != optim_idx:
                          continue
                      scaler.unscale_(optimizer)
  
              # TODO(kan-bayashi): Compute grad norm without clipping
              grad_norm = None
              if grad_clip > 0.0:
                  # compute the gradient norm to check if it is normal or not
                  grad_norm = torch.nn.utils.clip_grad_norm_(
                      model.parameters(),
                      max_norm=grad_clip,
                      norm_type=grad_clip_type,
                  )
                  # PyTorch<=1.4, clip_grad_norm_ returns float value
                  if not isinstance(grad_norm, torch.Tensor):
                      grad_norm = torch.tensor(grad_norm)
  
              if grad_norm is None or torch.isfinite(grad_norm):
                  all_steps_are_invalid = False
                  with reporter.measure_time(f"{turn}_optim_step_time"):
                      for iopt, (optimizer, scheduler) in enumerate(
                          zip(optimizers, schedulers)
                      ):
                          if (
                              optim_idx is not None
                              and iopt != optim_idx
                          ):
                              continue
                          if scaler is not None:
                              # scaler.step() first unscales the gradients of
                              # the optimizer's assigned params.
                              scaler.step(optimizer)
                              # Updates the scale for next iteration.
                              scaler.update()
                          else:
                              optimizer.step()
                          if isinstance(
                              scheduler, AbsBatchStepScheduler
                          ):
                              scheduler.step()
              else:
                  logging.warning(
                      f"The grad norm is {grad_norm}. "
                      "Skipping updating the model."
                  )
                  # Must invoke scaler.update() if unscale_() is used in the
                  # iteration to avoid the following error:
                  #   RuntimeError: unscale_() has already been called
                  #   on this optimizer since the last update().
                  # Note that if the gradient has inf/nan values,
                  # scaler.step skips optimizer.step().
                  if scaler is not None:
                      for iopt, optimizer in enumerate(optimizers):
                          if (
                              optim_idx is not None
                              and iopt != optim_idx
                          ):
                              continue
                          scaler.step(optimizer)
                          scaler.update()
  
              for iopt, optimizer in enumerate(optimizers):
                  # NOTE(kan-bayashi): In the case of GAN, we need to clear
                  #   the gradient of both optimizers after every update.
                  optimizer.zero_grad()
  
              # Register lr and train/load time[sec/step],
              # where step refers to accum_grad * mini-batch
              reporter.register(
                  {
                      f"optim{optim_idx}_lr{i}": pg["lr"]
                      for i, pg in enumerate(
                          optimizers[optim_idx].param_groups
                      )
                      if "lr" in pg
                  },
              )
              reporter.register(
                  {
                      f"{turn}_train_time": time.perf_counter()
                      - turn_start_time
                  }
              )
              turn_start_time = time.perf_counter()
  
          reporter.register(
              {"train_time": time.perf_counter() - start_time}
          )
          start_time = time.perf_counter()
  
          # NOTE(kamo): Call log_message() after next()
          reporter.next()
          if iiter % log_interval == 0:
              logging.info(reporter.log_message(-log_interval))
              if summary_writer is not None:
                  reporter.tensorboard_add_scalar(
                      summary_writer, -log_interval
                  )
              if use_wandb:
                  reporter.wandb_log()
  
      else:
          if distributed:
              iterator_stop.fill_(1)
              torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
  
      return all_steps_are_invalid
  
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 3 for calling the GANTrainer obj's underlying member method code with example usage: obj.train_one_epoch(cls, model, iterator, optimizers, schedulers, scaler, reporter, summary_writer, options, distributed_option) and returns: all_steps_are_invalid