code: def test_prediction_result_multilabel_with_enc_and_proba(self):
      all_labels = [[0], [0, 1], [2, 3]]
      enc = MultiLabelBinarizer()
      enc.fit(all_labels)
  
      proba = np.array(
          [
              [0.1, 0.2, 0.6, 0.1],
              [0.25, 0.25, 0.25, 0.25],
              [0.3, 0.3, 0.2, 0.2],
              [0.3, 0.2, 0.5, 0.1],
          ]
      )
  
      with self.assertWarnsRegex(
          DeprecationWarning,
          "The enc keyword argument has been deprecated since 1.1.0",
      ):
          result, proba_result = prediction_result(
              proba, True, proba.shape[1], enc=enc, return_proba=True
          )
      expected = csr_matrix(
          np.array(
              [
                  [0, 0, 1, 0],
                  [0, 0, 0, 0],
                  [0, 0, 0, 0],
                  [0, 0, 0, 0],
              ]
          )
      )
      assert_csr_matrix_equal(expected, result)
      expected_proba = csr_matrix(
          np.array(
              [
                  [0, 0, 0.6, 0],
                  [0, 0, 0, 0],
                  [0, 0, 0, 0],
                  [0, 0, 0, 0],
              ]
          )
      )
      assert_csr_matrix_equal(expected_proba, proba_result)
  
file_path: small-text/tests/unit/small_text/utils/test_classification.py
imports: import unittest
  import numpy
  from numpy.testing import assert_array_equal
  from scipy.sparse import csr_matrix
  from sklearn.preprocessing import MultiLabelBinarizer
  from small_text.utils.classification import empty_result, prediction_result
  from tests.utils.testing import assert_csr_matrix_equal
label_desc: Use image node_id 6 for calling the ClassificationUtilsTest obj's underlying member method code with example usage: obj.test_prediction_result_multilabel_with_enc_and_proba() without return types