code: def validate(self, validation_set):
      valid_loss = 0.0
      acc = 0.0
  
      self.model.eval()
      valid_iter = dataloader(
          validation_set.data,
          self.mini_batch_size,
          self._create_collate_fn(),
          train=False,
      )
  
      for x, masks, cls, weight, *_ in valid_iter:
          x, masks, cls = (
              x.to(self.device),
              masks.to(self.device),
              cls.to(self.device),
          )
          weight = weight.to(self.device)
  
          with torch.no_grad():
              outputs = self.model(x, attention_mask=masks)
              _, loss = self._compute_loss(cls, outputs)
              loss = loss * weight
              loss = loss.mean()
  
              valid_loss += loss.item()
              acc += self.sum_up_accuracy_(outputs.logits, cls)
              del outputs, x, masks, cls
  
      return valid_loss / len(validation_set), acc / len(validation_set)
  
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 13 for calling the TransformerBasedClassification obj's underlying member method code with example usage: obj.validate(validation_set) and returns: unknown,  unknown