code: def _train_loop_epoch(
      self,
      num_epoch,
      sub_train,
      sub_valid,
      weights,
      early_stopping,
      model_selection,
      optimizer,
      scheduler,
      tmp_dir,
  ):
      if self.memory_fix and (num_epoch + 1) % self.memory_fix == 0:
          torch.cuda.empty_cache()
  
      self.model.train()
      if self.validations_per_epoch > 1:
          num_batches = len(sub_train) // self.mini_batch_size + int(
              len(sub_train) % self.mini_batch_size > 0
          )
          if self.validations_per_epoch > num_batches:
              warnings.warn(
                  f"validations_per_epoch={self.validations_per_epoch} is greater than "
                  f"the maximum possible batches of {num_batches}",
                  RuntimeWarning,
              )
              validate_every = 1
          else:
              validate_every = int(
                  num_batches / self.validations_per_epoch
              )
      else:
          validate_every = None
  
      (
          train_loss,
          train_acc,
          valid_loss,
          valid_acc,
          stop,
      ) = self._train_loop_process_batches(
          num_epoch,
          sub_train,
          sub_valid,
          weights,
          early_stopping,
          model_selection,
          optimizer,
          scheduler,
          tmp_dir,
          validate_every=validate_every,
      )
  
      return train_acc, train_loss, valid_acc, valid_loss, stop
  
imports: import datetime
  import logging
  import tempfile
  import warnings
  import numpy
  from functools import partial
  from small_text.classifiers.classification import EmbeddingMixin
  from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
  from small_text.utils.classification import get_splits
  from small_text.utils.context import build_pbar_context
  from small_text.utils.data import check_training_data, list_length
  from small_text.utils.datetime import format_timedelta
  from small_text.utils.annotations import early_stopping_deprecation_warning, model_selection_deprecation_warning
  from small_text.utils.labels import get_num_labels
  from small_text.utils.logging import verbosity_logger, VERBOSITY_MORE_VERBOSE
  from small_text.utils.system import get_tmp_dir_base
label_desc: Use image node_id 7 for calling the TransformerBasedClassification obj's underlying member method code with example usage: obj._train_loop_epoch(num_epoch, sub_train, sub_valid, weights, early_stopping, model_selection, optimizer, scheduler, tmp_dir) and returns: train_acc,  train_loss,  valid_acc,  valid_loss,  stop