code: def get_sample(X_validation, y_validation):
      # too many samples in the data, down-sample it
      SAMPLES_LIMIT = 1000
      if X_validation.shape[0] > SAMPLES_LIMIT:
          X_validation.reset_index(inplace=True, drop=True)
          y_validation.reset_index(inplace=True, drop=True)
          X_vald = X_validation.sample(SAMPLES_LIMIT)
          y_vald = y_validation[X_vald.index]
      else:
          X_vald = X_validation
          y_vald = y_validation
      return X_vald, y_vald
  
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 3 for calling the PlotSHAP obj's underlying member method code with example usage: obj.get_sample(X_validation, y_validation) and returns: X_vald,  y_vald