code: def get_predictions(algorithm, X_vald, y_vald, ml_task):
      # compute predictions on down-sampled data
      predictions = algorithm.predict(X_vald)
  
      if ml_task == MULTICLASS_CLASSIFICATION:
          oh = OneHotEncoder(sparse=False)
          y_encoded = oh.fit_transform(np.array(y_vald).reshape(-1, 1))
          residua = np.sum(
              np.abs(np.array(y_encoded) - predictions), axis=1
          )
      else:
          residua = np.abs(np.array(y_vald) - predictions)
  
      df_preds = pd.DataFrame(
          {
              "res": residua,
              "lp": range(residua.shape[0]),
              "target": np.array(y_vald),
          },
          index=X_vald.index,
      )
      df_preds = df_preds.sort_values(by="res", ascending=False)
  
      return df_preds
  
imports: import logging
  import os
  import matplotlib.pyplot
  import numpy
  import pandas
  import shap
  from sklearn.preprocessing import OneHotEncoder
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
  from supervised.utils.config import LOG_LEVEL
  import warnings
label_desc: Use image node_id 4 for calling the PlotSHAP obj's underlying member method code with example usage: obj.get_predictions(algorithm, X_vald, y_vald, ml_task) and returns: df_preds