code: def sequential_api_model(num_classes):
      model = tf.keras.models.Sequential(
          [
              tf.keras.layers.Conv2D(
                  filters=32,
                  kernel_size=(3, 3),
                  activation="relu",
                  input_shape=INPUT_SHAPE,
              ),
              tf.keras.layers.Conv2D(
                  filters=64, kernel_size=(3, 3), activation="relu"
              ),
              tf.keras.layers.MaxPool2D(pool_size=(2, 2)),
              tf.keras.layers.Conv2D(
                  filters=32, kernel_size=(3, 3), activation="relu"
              ),
              tf.keras.layers.Conv2D(
                  filters=64,
                  kernel_size=(3, 3),
                  activation="relu",
                  name="grad_cam_target",
              ),
              tf.keras.layers.MaxPool2D(pool_size=(2, 2)),
              tf.keras.layers.Flatten(),
              tf.keras.layers.Dense(64, activation="relu"),
              tf.keras.layers.Dense(num_classes, activation="softmax"),
          ]
      )
      model.compile(
          optimizer="adam",
          loss="categorical_crossentropy",
          metrics=["accuracy"],
      )
  
      return model
  
file_path: tf-explain/tests/integration/test_keras_api.py
imports: import numpy
  import pytest
  import tensorflow
  import tf_explain
label_desc: Use image node_id 2 for calling a global function with example usage: sequential_api_model(num_classes) and returns: model