code: def _predict_proba(self, dataset_iter, logits_transform):
      predictions = np.empty((0, self.num_classes), dtype=float)
      for text, masks, *_ in dataset_iter:
          text, masks = text.to(self.device), masks.to(self.device)
          outputs = self.model(text, attention_mask=masks)
  
          predictions = np.append(
              predictions,
              logits_transform(outputs.logits).to("cpu").numpy(),
              axis=0,
          )
          del text, masks
      return predictions
  
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 16 for calling the TransformerBasedClassification obj's underlying member method code with example usage: obj._predict_proba(dataset_iter, logits_transform) and returns: predictions