code: def multiclass_postprocess_RESTORE_THIS(
      X_binned, feature_graphs, binned_predict_proba, feature_types
  ):
      """Postprocesses multiclass model graphs with desired properties.
  
      Args:
          X_binned: Training dataset, pre-binned. Contains integer values, 0+. Each value is a unique bin.
          feature_graphs: List of 2d numpy arrays. List is size d for d features. Each numpy array is of size b for b bins in the feature. Each bin has k elements for k classes.
          binned_predict_proba: Function that takes in X_binned, returns 2d numpy array of predictions. Each row in the return vector has k elements, with probability of belonging to class k.
          feature_types: List of strings "nominal" or "ordinal" or "continuous" for each feature.
  
      Returns:
          Dictionary with updated model graphs and new intercepts.
      """
  
      updated_feature_graphs = copy.deepcopy(feature_graphs)
      K = feature_graphs[0].shape[1]
  
      # Compute the predicted probability on the original data.
      predprob = binned_predict_proba(X_binned)
      predprob_prev = [None] * len(feature_graphs)
  
      # Compute the predicted probability on the counterfactual data with each value in feature i decrease by 1.
      for i in range(len(feature_graphs)):
          data_prev = np.copy(X_binned)
          data_prev[i, :] = np.maximum(X_binned[i, :] - 1, 0)
          predprob_prev[i] = binned_predict_proba(data_prev)
  
      intercepts = np.zeros(K)
      for i in range(len(feature_graphs)):
          bincount = np.bincount(X_binned[i, :].astype(int))
          if feature_types[i] == "continuous":
              num_bins = feature_graphs[i].shape[0]
              change = np.zeros(num_bins)
              for v in range(1, num_bins):
                  subset_index = X_binned[i, :] == v
                  ratio = np.divide(
                      predprob[subset_index, :],
                      predprob_prev[i][subset_index, :],
                  )
                  sum_ratio = np.sum(np.subtract(ratio, 1), axis=0)
                  difference = (
                      feature_graphs[i][v, :]
                      - feature_graphs[i][v - 1, :]
                      + change[v - 1]
                  )
                  change[v] = np.mean(difference)
                  new_difference = difference - change[v]
                  back_change = 0
                  for k in range(K):
                      if new_difference[k] * sum_ratio[k] < 0 and abs(
                          back_change
                      ) < abs(new_difference[k]):
                          back_change = new_difference[k]
                  change[v] = change[v] + back_change
              updated_feature_graphs[i] = np.subtract(
                  updated_feature_graphs[i],
                  change.reshape((num_bins, -1)),
              )
          for k in range(K):
              mean = (
                  np.sum(
                      np.multiply(
                          updated_feature_graphs[i][:, k], bincount
                      )
                  )
                  / X_binned.shape[1]
              )
              updated_feature_graphs[i][:, k] = np.subtract(
                  updated_feature_graphs[i][:, k], mean
              )
              intercepts[k] += mean
      return {
          "feature_graphs": updated_feature_graphs,
          "intercepts": intercepts,
      }
  
label_desc: Use image node_id 1 for calling a global function with example usage: multiclass_postprocess_RESTORE_THIS(X_binned, feature_graphs, binned_predict_proba, feature_types) and returns: dict