Neural network with linear activation output. Calculate output range for each of the output neurons

Let's suposse I have a neural network like the following:

model = keras.models.Sequential()
model.add(keras.layers.Dense(10, input_shape=(5,), activation='relu'))
model.add(keras.layers.Dense(4, activation='linear'))

With n output neurons with a linear activation function.

The train process is not important here, so we can take a look to the random weights that keras initialized using:

model.weights 

Of course, in a real example these weights should be adjusted in the training process.

Depending on these model.weights, each of the output neurons returns values in a range.

I would like to calculate this exact range.

Does keras offer any function to calculate it?

I built a flawed piece of code to make an aproximation of it, using a loop and predicting random inputs. But this would not be really usable in a real example with much more inputs/neurons/weights.

Here a few examples trying to clarify my question (All of them assume that the input values are between and 1):

model = keras.models.Sequential()
model.add(keras.layers.Dense(1, input_shape=(2,), activation='linear', use_bias=False))

model.set_weights([np.array([1, 1]).reshape(2, 1)]) 

For the previous example the output neuron results would be between 0 and 2

model.set_weights([np.array([-0.5, 1]).reshape(2, 1)])

For the previous example the output neuron results would be between -0.5 and 1

model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(2,), activation='linear', use_bias=False))
model.add(keras.layers.Dense(1, activation='linear', use_bias=False))

model.set_weights([np.array([1, 1, 1, 1]).reshape(2,2), np.array([1, 1]).reshape(2,1)])

For the previous example the output neuron results would be between 0 and 4

These are simplified examples. In a real scenario with a much complex network structure, activation functions, bias..... these ranges are not obvious to calculate.

from Neural network with linear activation output. Calculate output range for each of the output neurons