【简单的基于循环神经网络（RNN）的模型（深度学习经典代码实现）】

import torch
#Code – Parameters
input_size = 4
hidden_size = 4
num_layers = 1
batch_size = 1
seq_len = 5
#Code – Prepare Data
idx2char = ['e', 'h', 'l', 'o']
x_data = [1, 0, 2, 2, 3]
y_data = [3, 1, 2, 3, 2]
one_hot_lookup = [[1, 0, 0, 0],
                  [0, 1, 0, 0],
                  [0, 0, 1, 0],
                  [0, 0, 0, 1]]
x_one_hot = [one_hot_lookup[x] for x in x_data]
inputs = torch.Tensor(x_one_hot).view(seq_len, batch_size, input_size)
labels = torch.LongTensor(y_data)
#Code – Design Model
class Model(torch.nn.Module):
    def __init__(self, input_size, hidden_size, batch_size, num_layers=1):
        super(Model, self