I would like to understand the reason behind doing floor division by 2 to find the number of layers in the neural network:

```
def L_model_forward(X, parameters):
"""
Implement forward propagation for the [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID computation
Arguments:
X -- data, numpy array of shape (input size, number of examples)
parameters -- output of initialize_parameters_deep()
Returns:
AL -- activation value from the output (last) layer
caches -- list of caches containing:
every cache of linear_activation_forward() (there are L of them, indexed from 0 to L-1)
"""
caches = []
A = X
L = len(parameters) // 2 # number of layers in the neural network
```