code: def _compute_loss(self, cls, outputs):
      if self.num_classes == 2:
          logits = outputs.logits
          target = F.one_hot(cls, 2).float()
      else:
          logits = outputs.logits.view(-1, self.num_classes)
          target = cls
      loss = self.criterion(logits, target)
  
      return logits, loss
  
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 11 for calling the TransformerBasedClassification obj's underlying member method code with example usage: obj._compute_loss(cls, outputs) and returns: logits,  loss