code: def empty_result(
      multi_label,
      num_classes,
      return_prediction=True,
      return_proba=True,
  ):
      """Helper method which returns an empty classification result. This ensures that all results
      have the correct dtype.
  
      At least one of `prediction` and `proba` must be `True`.
  
      Parameters
      ----------
      multi_label : bool
          Indicates a multi-label setting if `True`, otherwise a single-label setting
          when set to `False`.
      num_classes : int
          The number of classes.
      return_prediction : bool, default=True
          If `True`, returns an empty result of prediction.
      return_proba : bool, default=True
          If `True`, returns an empty result of probabilities.
  
      Returns
      -------
      predictions : np.ndarray[np.int64]
          An empty ndarray of predictions if `return_prediction` is True.
      proba : np.ndarray[float]
          An empty ndarray of probabilities if `return_proba` is True.
      """
      if not return_prediction and not return_proba:
          raise ValueError(
              "Invalid usage: At least one of 'prediction' or 'proba' must be True"
          )
  
      elif multi_label:
          if return_prediction and return_proba:
              return csr_matrix(
                  (0, num_classes), dtype=np.int64
              ), csr_matrix((0, num_classes), dtype=float)
          elif return_prediction:
              return csr_matrix((0, num_classes), dtype=np.int64)
          else:
              return csr_matrix((0, num_classes), dtype=float)
      else:
          if return_prediction and return_proba:
              return np.empty(
                  (0, num_classes), dtype=np.int64
              ), np.empty(shape=(0, num_classes), dtype=float)
          elif return_prediction:
              return np.empty((0, num_classes), dtype=np.int64)
          else:
              return np.empty((0, num_classes), dtype=float)
  
file_path: small-text/small_text/utils/classification.py
imports: import numpy
  from scipy.sparse import csr_matrix
  from small_text.data.datasets import split_data
  from small_text.utils.annotations import prediction_result_enc_warning
  from small_text.utils.labels import list_to_csr
label_desc: Use image node_id 3 for calling a global function with example usage: empty_result(multi_label, num_classes, return_prediction, return_proba) and returns: csr_matrix,  csr_matrix, np,  np, csr_matrix, csr_matrix, np, np