code: def test_ShapDeepExplainer(self):
      batch_size = 128
      num_classes = 10
      epochs = 2
  
      # input image dimensions
      img_rows, img_cols = 28, 28
  
      # the data, split between train and test sets
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
  
      if K.image_data_format() == "channels_first":
          x_train = x_train.reshape(
              x_train.shape[0], 1, img_rows, img_cols
          )
          x_test = x_test.reshape(
              x_test.shape[0], 1, img_rows, img_cols
          )
          input_shape = (1, img_rows, img_cols)
      else:
          x_train = x_train.reshape(
              x_train.shape[0], img_rows, img_cols, 1
          )
          x_test = x_test.reshape(
              x_test.shape[0], img_rows, img_cols, 1
          )
          input_shape = (img_rows, img_cols, 1)
  
      x_train = x_train.astype("float32")
      x_test = x_test.astype("float32")
      x_train /= 255
      x_test /= 255
      print("x_train shape:", x_train.shape)
      print(x_train.shape[0], "train samples")
      print(x_test.shape[0], "test samples")
  
      y_train = keras.utils.to_categorical(y_train, num_classes)
      y_test = keras.utils.to_categorical(y_test, num_classes)
  
      model = Sequential()
      model.add(
          Conv2D(
              32,
              kernel_size=(3, 3),
              activation="relu",
              input_shape=input_shape,
          )
      )
      model.add(Conv2D(64, (3, 3), activation="relu"))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.25))
      model.add(Flatten())
      model.add(Dense(128, activation="relu"))
      model.add(Dropout(0.5))
      model.add(Dense(num_classes, activation="softmax"))
  
      model.compile(
          loss=keras.losses.categorical_crossentropy,
          optimizer=keras.optimizers.Adadelta(),
          metrics=["accuracy"],
      )
  
      model.fit(
          x_train,
          y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test),
      )
      score = model.evaluate(x_test, y_test, verbose=0)
      print("Test loss:", score[0])
      print("Test accuracy:", score[1])
  
      # select a set of background examples to take an expectation over
      background = x_train[
          np.random.choice(x_train.shape[0], 100, replace=False)
      ]
  
      # explain predictions of the model on three images
      e = DeepExplainer(model, background)
  
      shap_values = e.explain_instance(x_test[1:5])
      print("Invoked Shap DeepExplainer")
  
imports: from __future__ import print_function
  import unittest
  import sklearn
  import sklearn.datasets
  import sklearn.ensemble
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.model_selection import train_test_split
  import numpy
  import keras
  from keras.applications.vgg16 import VGG16
  from keras.applications.vgg16 import preprocess_input, decode_predictions
  from keras.datasets import mnist
  from keras.models import Sequential
  from keras.layers import Dense, Dropout, Flatten
  from keras.layers import Conv2D, MaxPooling2D
  import keras.backend
  import json
  import xgboost
  from aix360.algorithms.shap import KernelExplainer, LinearExplainer, GradientExplainer, DeepExplainer, TreeExplainer
  import shap
label_desc: Use image node_id 4 for calling the TestShapExplainer obj's underlying member method code with example usage: obj.test_ShapDeepExplainer() without return types