code: def _train_single_batch(self, x, masks, cls, weight, optimizer):
      train_loss = 0.0
      train_acc = 0.0
  
      optimizer.zero_grad()
  
      x, masks, cls = (
          x.to(self.device),
          masks.to(self.device),
          cls.to(self.device),
      )
      weight = weight.to(self.device)
  
      outputs = self.model(x, attention_mask=masks)
  
      logits, loss = self._compute_loss(cls, outputs)
      loss = loss * weight
      loss = loss.mean()
  
      loss.backward()
  
      torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
  
      optimizer.step()
  
      train_loss += loss.detach().item()
      train_acc += self.sum_up_accuracy_(logits, cls)
  
      del x, masks, cls, loss, outputs
  
      return train_loss, train_acc
  
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 10 for calling the TransformerBasedClassification obj's underlying member method code with example usage: obj._train_single_batch(x, masks, cls, weight, optimizer) and returns: train_loss,  train_acc