Constructing a keras model

I don't understand what's happening in this code:

def construct_model(use_imagenet=True):
    # line 1: how do we keep all layers of this model ?
    model = keras.applications.InceptionV3(include_top=False, input_shape=(IMG_SIZE, IMG_SIZE, 3),
                                          weights='imagenet' if use_imagenet else None) # line 1: how do we keep all layers of this model ?

    new_output = keras.layers.GlobalAveragePooling2D()(model.output)

    new_output = keras.layers.Dense(N_CLASSES, activation='softmax')(new_output)
    model = keras.engine.training.Model(model.inputs, new_output)
    return model

Specifically, my confusion is, when we call the last constructor

model = keras.engine.training.Model(model.inputs, new_output)

we specify input layer and output layer, but how does it know we want all the other layers to stay?

In other words, we append the new_output layer to the pre-trained model we load in line 1, that is the new_output layer, and then in the final constructor (final line), we just create and return a model with a specified input and output layers, but how does it know what other layers we want in between?

Side question 1): What is the difference between keras.engine.training.Model and keras.models.Model?

Side question 2): What exactly happens when we do new_layer = keras.layers.Dense(...)(prev_layer)? Does the () operation return new layer, what does it do exactly?

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