docstr: Parameters
  ----------
  data_set : small_text.data.Dataset
      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 multi-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, data_set, return_proba=False):
      """
      Parameters
      ----------
      data_set : small_text.data.Dataset
          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 multi-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.
      """
      if len(data_set) == 0:
          return empty_result(
              self.multi_label,
              self.num_classes,
              return_prediction=True,
              return_proba=return_proba,
          )
  
      proba = self.predict_proba(data_set)
      predictions = prediction_result(
          proba, self.multi_label, self.num_classes
      )
  
      if return_proba:
          return predictions, proba
  
      return predictions
  
imports: import logging
  import warnings
  from abc import abstractmethod
  from functools import partial
  from pathlib import Path
  from small_text.classifiers.classification import Classifier
  from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
  from small_text.training.early_stopping import EarlyStopping, NoopEarlyStopping, EarlyStoppingOrCondition
  from small_text.training.metrics import Metric
  from small_text.training.model_selection import ModelSelection, NoopModelSelection
  from small_text.utils.classification import empty_result, prediction_result
label_desc: Use image node_id 3 for calling the PytorchClassifier obj's underlying member method code with example usage: obj.predict(data_set, return_proba) and returns: predictions, empty_result, predictions,  proba