docstr: Predicts the labels for the given dataset.
  
  Parameters
  ----------
  dataset : PytorchTextClassificationDataset
      A dataset on whose instances predictions are made.
  return_proba : bool
      If True, additionally returns the confidence distribution over all classes.
  
  Returns
  -------
  predictions : np.ndarray[np.int32] or csr_matrix[np.int32]
      List of predictions if the classifier was fitted on single-label data,
      otherwise a sparse matrix of predictions.
  probas : np.ndarray[np.float32] (optional)
      List of probabilities (or confidence estimates) if `return_proba` is True.
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code: def predict(self, dataset, return_proba=False):
      """Predicts the labels for the given dataset.
  
      Parameters
      ----------
      dataset : PytorchTextClassificationDataset
          A dataset on whose instances predictions are made.
      return_proba : bool
          If True, additionally returns the confidence distribution over all classes.
  
      Returns
      -------
      predictions : np.ndarray[np.int32] or csr_matrix[np.int32]
          List of predictions if the classifier was fitted on single-label data,
          otherwise a sparse matrix of predictions.
      probas : np.ndarray[np.float32] (optional)
          List of probabilities (or confidence estimates) if `return_proba` is True.
      """
      return super().predict(dataset, return_proba=return_proba)
  
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 11 for calling the KimCNNClassifier obj's underlying member method code with example usage: obj.predict(dataset, return_proba) and returns: super