def mse(y_true, y_pred):
    y_pred = y_pred.reshape(y_true.size())
    loss = torch.nn.MSELoss()
    output = loss(y_pred, y_true)
    return output.data

def encode(words):
    # Just change this variables name :)
    d0_size = len(words)
    d1_size = max([len(word) for word in words])
    d2_size = 26
    tensor = torch.zeros(d0_size, d1_size,d2_size)
    for word_idx,word in enumerate(words):
        for letter_idx,letter in enumerate(word):
            tensor[word_idx,letter_idx,ord(letter)-ord('a')] = 1
    return tensor