Looking at the following diagram and the code you wrote , which is :

import torch.nn as nn

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(RNN, self).__init__()

        self.hidden_size = hidden_size

        self.i2h = 

I’m trying to understand how eneyj is implemented, and a lot of things seem straightforward enough (builtin Z33, same: stringify operands then check string equality; evaluate Z10, list: evaluate head and tail), but the “alpha” and “beta” family of functions look intimidating, and I haven’t found any documentation or comments for them yet. @vrandezo is there some way to summarize what they do or wh

NYPM needs a logo.... but we should def first change the favicon to not be the react logo -___-