code: def simple_seq():
      """A simple sequential model for images."""
      model = Sequential(
          [
              Activation(
                  "linear", input_shape=(32, 32, 1)
              ),  # index 0, input
              conv_layer,  # index 1, conv
              Conv2D(20, (3, 3)),  # index 2, conv2
              GlobalAveragePooling2D(),  # index 3, gap
              # output shape is (None, 20)
          ]
      )
      print("Summary of model:")
      model.summary()
      # rename layers
      for i, layer in enumerate(model.layers):
          layer.name = "layer%d" % i
      return model
  
imports: import pytest
  import keras.backend
  from keras.models import Sequential, Model
  from keras.layers import Dense, Activation, Conv2D, GlobalAveragePooling2D, Input, Lambda
  from keras.backend import epsilon
  import numpy
  from eli5.keras.explain_prediction import explain_prediction, _validate_doc, _get_activation_layer
  from eli5.keras.gradcam import _get_target_prediction, _calc_gradient, gradcam
label_desc: Use image node_id 1 for calling a global function with example usage: simple_seq() and returns: model