why do we use dense layer in cnn code example

Example 1: Dense layer

Dense is the only actual network layer in that model.

A Dense layer feeds all outputs from the previous layer to all its neurons, each neuron providing one output to the next layer.

It's the most basic layer in neural networks.

A Dense(10) has ten neurons. A Dense(512) has 512 neurons.

Example 2: dense layer keras

>>> # Create a `Sequential` model and add a Dense layer as the first layer.  
>>> model = tf.keras.models.Sequential()
>>> model.add(tf.keras.Input(shape=(16,)))
>>> model.add(tf.keras.layers.Dense(32, activation='relu'))
>>> # Now the model will take as input arrays of shape (None, 16)  
>>> # and output arrays of shape (None, 32).  
>>> # Note that after the first layer, you don't need to specify  
>>> # the size of the input anymore:  
>>> model.add(tf.keras.layers.Dense(32))
>>> model.output_shape
(None, 32)