code: def _predict_proba_dropout_sampling(
      self, dataset_iter, logits_transform, dropout_samples=2
  ):
      predictions = np.empty(
          (0, dropout_samples, self.num_classes), dtype=float
      )
  
      with enable_dropout(self.model):
          for text, *_ in dataset_iter:
              batch_size, vector_len = text.shape
              full_size = batch_size * dropout_samples
              text = text.to(self.device)
              text = text.repeat(1, dropout_samples).resize(
                  full_size, vector_len
              )
  
              output = self.model.forward(text)
  
              prediction_for_batch = logits_transform(output)
              prediction_for_batch = prediction_for_batch.unsqueeze(
                  dim=1
              ).resize(batch_size, dropout_samples, self.num_classes)
  
              predictions = np.append(
                  predictions,
                  prediction_for_batch.to("cpu").numpy(),
                  axis=0,
              )
              del text
  
      return predictions
  
imports: import datetime
  import logging
  import tempfile
  import numpy
  from functools import partial
  from sklearn.preprocessing import MultiLabelBinarizer
  from small_text.classifiers.classification import EmbeddingMixin
  from small_text.integrations.pytorch.classifiers.base import PytorchClassifier
  from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
  from small_text.integrations.pytorch.models.kimcnn import KimCNN
  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.annotations import early_stopping_deprecation_warning
  from small_text.utils.labels import csr_to_list, get_num_labels
  from small_text.utils.datetime import format_timedelta
  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 14 for calling the KimCNNClassifier obj's underlying member method code with example usage: obj._predict_proba_dropout_sampling(dataset_iter, logits_transform, dropout_samples) and returns: predictions