code: def fix_multiclass_predict_proba(
      y_proba,  # type: np.ndarray
      seen_classes,
      complete_classes,
  ):
      # type: (...) -> np.ndarray
      """
      Add missing columns to predict_proba result.
  
      When a multiclass classifier is fit on a dataset which only contains
      a subset of possible classes its predict_proba result only has columns
      corresponding to seen classes. This function adds missing columns.
      """
      assert set(complete_classes) >= set(seen_classes)
      y_proba_fixed = np.zeros(
          shape=(y_proba.shape[0], len(complete_classes)),
          dtype=y_proba.dtype,
      )
      class_mapping = np.searchsorted(complete_classes, seen_classes)
      y_proba_fixed[:, class_mapping] = y_proba
      return y_proba_fixed
  
imports: from __future__ import absolute_import
  from typing import List, Any
  import numpy
  from scipy.stats import entropy
  from sklearn.pipeline import Pipeline
  from sklearn.utils import check_random_state, issparse
  from sklearn.utils.metaestimators import if_delegate_has_method
  from sklearn.utils import shuffle
  from eli5.utils import vstack
  from eli5.sklearn.utils import sklearn_version
label_desc: Use image node_id 3 for calling a global function with example usage: fix_multiclass_predict_proba(y_proba, seen_classes, complete_classes) and returns: y_proba_fixed