Suppose that we train a neural network to classify images. The inputs are 3-by-3 grayscale images there are places in each image), and the labels contain 10 classes. Connected with the input layer with the image as a flattened 1-dimensional vector), there are 5 nodes in the first hidden layer, 4 in the second hidden layer, and then followed with the max output layer. Each node is given an activation function of RelU(X). How many trainable parameters are there in this neural networld