input: Write a python function to build a simple Convolutional Neural Network (CNN) model using Keras.
  assert build_cnn_model((64, 64, 3)).count_params() == 147778
  assert len(build_cnn_model((64, 64, 3)).layers) == 8
output: def build_cnn_model(input_shape):
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
  
      model = Sequential()
      model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
      model.add(MaxPooling2D((2, 2)))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D((2, 2)))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(Flatten())
      model.add(Dense(64, activation='relu'))
      model.add(Dense(10, activation='softmax'))
      
      return model