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 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: TrainerOptions,
      distributed_option: DistributedOption,
  ) -> bool:
      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
      create_graph_in_tensorboard = options.create_graph_in_tensorboard
      distributed = distributed_option.distributed
  
      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, (utt_id, 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["utt_id"] = utt_id
  
          batch = to_device(batch, "cuda" if ngpu > 0 else "cpu")
          if no_forward_run:
              all_steps_are_invalid = False
              continue
  
          if (
              create_graph_in_tensorboard
              and iiter == 1
              and summary_writer is not None
          ):
              if distributed:
                  _model = getattr(model, "module")
              else:
                  _model = model
                  if _model is not None:
                      try:
                          _args = kwargs2args(_model.forward, batch)
                      except (ValueError, TypeError):
                          logging.warning(
                              "inpect.signature() is failed for the model. "
                              "The graph can't be added for tensorboard."
                          )
                      else:
                          try:
                              summary_writer.add_graph(
                                  _model, _args, use_strict_trace=False
                              )
                          except Exception:
                              logging.warning(
                                  "summary_writer.add_graph() "
                                  "is failed for the model. "
                                  "The graph can't be added for tensorboard."
                              )
                          del _args
                  else:
                      logging.warning(
                          "model.module is not found (This should be a bug.)"
                      )
              del _model
  
          with autocast(
              scaler is not None,
              **autocast_args,
          ):
              with reporter.measure_time("forward_time"):
                  retval = model(**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:
                      loss, stats, weight = retval
                      optim_idx = None
  
              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 because DistributedDataParallel
                  # automatically normalizes the gradient by world_size.
                  loss *= torch.distributed.get_world_size()
  
              loss /= accum_grad
  
          reporter.register(stats, weight)
  
          with reporter.measure_time("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 iiter % accum_grad == 0:
              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)
  
              # gradient noise injection
              if grad_noise:
                  add_gradient_noise(
                      model,
                      reporter.get_total_count(),
                      duration=100,
                      eta=1.0,
                      scale_factor=0.55,
                  )
  
              # 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 not torch.isfinite(grad_norm):
                  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()
  
              else:
                  reporter.register(
                      {
                          "grad_norm": grad_norm,
                          "clip": torch.where(
                              grad_norm > grad_clip,
                              grad_norm.new_tensor(100),
                              grad_norm.new_tensor(0),
                          ),
                          "loss_scale": scaler.get_scale()
                          if scaler
                          else 1.0,
                      }
                  )
                  all_steps_are_invalid = False
                  with reporter.measure_time("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()
              for iopt, optimizer in enumerate(optimizers):
                  if optim_idx is not None and iopt != optim_idx:
                      continue
                  optimizer.zero_grad()
  
              # Register lr and train/load time[sec/step],
              # where step refers to accum_grad * mini-batch
              reporter.register(
                  dict(
                      {
                          f"optim{i}_lr{j}": pg["lr"]
                          for i, optimizer in enumerate(optimizers)
                          for j, pg in enumerate(optimizer.param_groups)
                          if "lr" in pg
                      },
                      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 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 6 for calling the Trainer 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