class_docstr: Trainer.
  Designed for speaker recognition.
  Training will be done as closed set classification.
  Validation will be open set EER calculation.
class_docstr_tok: ["Trainer",".","Designed","for","speaker","recognition",".","Training","will","be","done","as","closed","set","classification",".","Validation","will","be","open","set","EER","calculation","."]
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()
  
      scores = []
      labels = []
      spk_embd_dic = {}
      bs = 0
  
      # [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")
  
      # fill dictionary with speech samples
      utt_id_list = []
      speech_list = []
      task_token = None
      for utt_id, batch in iterator:
          bs = max(bs, len(utt_id))
          if "task_tokens" in batch:
              task_token = batch["task_tokens"][0]
  
          assert isinstance(batch, dict), type(batch)
          for _utt_id, _speech, _speech2 in zip(
              utt_id, batch["speech"], batch["speech2"]
          ):
              _utt_id_1, _utt_id_2 = _utt_id.split("*")
              if _utt_id_1 not in utt_id_list:
                  utt_id_list.append(_utt_id_1)
                  speech_list.append(
                      to_device(_speech, "cuda" if ngpu > 0 else "cpu")
                  )
              if _utt_id_2 not in utt_id_list:
                  utt_id_list.append(_utt_id_2)
                  speech_list.append(
                      to_device(_speech2, "cuda" if ngpu > 0 else "cpu")
                  )
  
      # extract speaker embeddings.
      n_utt = len(utt_id_list)
      for ii in range(0, n_utt, bs):
          _utt_ids = utt_id_list[ii : ii + bs]
          _speechs = speech_list[ii : ii + bs]
          _speechs = torch.stack(_speechs, dim=0)
          org_shape = (_speechs.size(0), _speechs.size(1))
          _speechs = _speechs.flatten(0, 1)
          _speechs = to_device(_speechs, "cuda" if ngpu > 0 else "cpu")
  
          if task_token is None:
              task_tokens = None
          else:
              task_tokens = to_device(
                  task_token.repeat(_speechs.size(0)),
                  "cuda" if ngpu > 0 else "cpu",
              ).unsqueeze(1)
          spk_embds = model(
              speech=_speechs,
              spk_labels=None,
              extract_embd=True,
              task_tokens=task_tokens,
          )
          spk_embds = F.normalize(spk_embds, p=2, dim=1)
          spk_embds = spk_embds.view(org_shape[0], org_shape[1], -1)
  
          for _utt_id, _spk_embd in zip(_utt_ids, spk_embds):
              spk_embd_dic[_utt_id] = _spk_embd
  
      del utt_id_list
      del speech_list
  
      # calculate similarity scores
      for utt_id, batch in iterator:
          batch["spk_labels"] = to_device(
              batch["spk_labels"], "cuda" if ngpu > 0 else "cpu"
          )
  
          if distributed:
              torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
              if iterator_stop > 0:
                  break
  
          for _utt_id in utt_id:
              _utt_id_1, _utt_id_2 = _utt_id.split("*")
              score = torch.cdist(
                  spk_embd_dic[_utt_id_1], spk_embd_dic[_utt_id_2]
              )
              score = -1.0 * torch.mean(score)
              scores.append(
                  score.view(1)
              )  # 0-dim to 1-dim tensor for cat
          labels.append(batch["spk_labels"])
  
      else:
          if distributed:
              iterator_stop.fill_(1)
              torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
      torch.cuda.empty_cache()
  
      scores = torch.cat(scores).type(torch.float32)
      labels = torch.cat(labels).type(torch.int32).flatten()
  
      if distributed:
          # get the number of trials assigned on each GPU
          length = to_device(
              torch.tensor([labels.size(0)], dtype=torch.int32), "cuda"
          )
          lengths_all = [
              to_device(torch.zeros(1, dtype=torch.int32), "cuda")
              for _ in range(torch.distributed.get_world_size())
          ]
          torch.distributed.all_gather(lengths_all, length)
  
          scores_all = [
              to_device(torch.zeros(i, dtype=torch.float32), "cuda")
              for i in lengths_all
          ]
          torch.distributed.all_gather(scores_all, scores)
          scores = torch.cat(scores_all)
  
          labels_all = [
              to_device(torch.zeros(i, dtype=torch.int32), "cuda")
              for i in lengths_all
          ]
          torch.distributed.all_gather(labels_all, labels)
          labels = torch.cat(labels_all)
          rank = torch.distributed.get_rank()
          torch.distributed.barrier()
      scores = scores.detach().cpu().numpy()
      labels = labels.detach().cpu().numpy()
  
      # calculate statistics in target and nontarget classes.
      n_trials = len(scores)
      scores_trg = []
      scores_nontrg = []
      for _s, _l in zip(scores, labels):
          if _l == 1:
              scores_trg.append(_s)
          elif _l == 0:
              scores_nontrg.append(_s)
          else:
              raise ValueError(f"{_l}, {type(_l)}")
      trg_mean = float(np.mean(scores_trg))
      trg_std = float(np.std(scores_trg))
      nontrg_mean = float(np.std(scores_nontrg))
      nontrg_std = float(np.std(scores_nontrg))
  
      # exception for collect_stats.
      if len(scores) == 1:
          reporter.register(stats=dict(eer=1.0, mindcf=1.0))
          return
  
      # predictions, ground truth, and the false acceptance rates to calculate
      results = tuneThresholdfromScore(scores, labels, [1, 0.1])
      eer = results[1]
      fnrs, fprs, thresholds = ComputeErrorRates(scores, labels)
  
      # p_target, c_miss, and c_falsealarm in NIST minDCF calculation
      p_trg, c_miss, c_fa = 0.05, 1, 1
      mindcf, _ = ComputeMinDcf(
          fnrs, fprs, thresholds, p_trg, c_miss, c_fa
      )
  
      reporter.register(
          stats=dict(
              eer=eer,
              mindcf=mindcf,
              n_trials=n_trials,
              trg_mean=trg_mean,
              trg_std=trg_std,
              nontrg_mean=nontrg_mean,
              nontrg_std=nontrg_std,
          )
      )
  
      # added to reduce GRAM usage. May have minor speed boost when
      # this line is commented in case GRAM is not fully used.
      torch.cuda.empty_cache()
  
imports: import argparse
  import dataclasses
  import logging
  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.functional
  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.train.trainer import Trainer, TrainerOptions
  from espnet2.utils.build_dataclass import build_dataclass
  from espnet2.utils.eer import ComputeErrorRates, ComputeMinDcf, tuneThresholdfromScore
  from espnet2.utils.kwargs2args import kwargs2args
label_desc: Use image node_id 2 for calling the SpkTrainer obj's underlying member method code with example usage: obj.validate_one_epoch(cls, model, iterator, reporter, options, distributed_option) without return types