input: Write a Python function to create a convolutional neural network using TensorFlow.
  assert isinstance(create_cnn_model((150,150,3)), tf.keras.Sequential)
  assert create_cnn_model((150,150,3)).layers[0].input_shape == (None, 150, 150, 3)
  assert create_cnn_model((150,150,3)).layers[-1].output_shape == (None, 4)
output: def create_cnn_model(input_shape):
      import tensorflow as tf
      from tensorflow.keras import layers, models
      
      model = models.Sequential()
      model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
      model.add(layers.MaxPooling2D((2, 2)))
      model.add(layers.Conv2D(64, (3, 3), activation='relu'))
      model.add(layers.MaxPooling2D((2, 2)))
      model.add(layers.Conv2D(128, (3, 3), activation='relu'))
      model.add(layers.MaxPooling2D((2, 2)))
      model.add(layers.Conv2D(128, (3, 3), activation='relu'))
      model.add(layers.MaxPooling2D((2, 2)))
      
      model.add(layers.Flatten())
      model.add(layers.Dense(512, activation='relu'))
      model.add(layers.Dense(4, activation='softmax'))
      
      return model