docstr: Predicts the label distributions.
  
  Parameters
  ----------
  dataset : PytorchTextClassificationDataset
      A dataset whose labels will be predicted.
  dropout_sampling : int
      If `dropout_sampling > 1` then all dropout modules will be enabled during prediction and
      multiple rounds of predictions will be sampled for each instance.
  
  Returns
  -------
  scores : np.ndarray
      Distribution of confidence scores over all classes of shape (num_samples, num_classes).
      If `dropout_sampling > 1` then the shape is (num_samples, dropour_samples, num_classes).
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code: def predict_proba(self, dataset, dropout_sampling=1):
      """Predicts the label distributions.
  
      Parameters
      ----------
      dataset : PytorchTextClassificationDataset
          A dataset whose labels will be predicted.
      dropout_sampling : int
          If `dropout_sampling > 1` then all dropout modules will be enabled during prediction and
          multiple rounds of predictions will be sampled for each instance.
  
      Returns
      -------
      scores : np.ndarray
          Distribution of confidence scores over all classes of shape (num_samples, num_classes).
          If `dropout_sampling > 1` then the shape is (num_samples, dropour_samples, num_classes).
      """
      return super().predict_proba(
          dataset, dropout_sampling=dropout_sampling
      )
  
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 12 for calling the KimCNNClassifier obj's underlying member method code with example usage: obj.predict_proba(dataset, dropout_sampling) and returns: super