👉 A skeletonizer is a type of neural network architecture that is designed to reduce the computational complexity of deep learning models. It consists of two main components: the input layer and the output layer. The input layer receives data inputs from the user, while the output layer produces outputs for the model's predictions. The input layer can be any type of input, such as images or audio signals, but it is typically a convolutional layer that performs feature extraction on the input data. The output layer processes the