请为我写一个使用pytorch中的RNN对于时间序列数据进行预测的代码

import torch
import torch.nn as nnclass RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(RNN, self).__init__()

self.hidden_size = hidden_size

self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
self.i2o = nn.Linear(input_size + hidden_size, output_size)
self.softmax = nn.LogSoftmax(dim=1)

def forward(self, input, hidden):
combined = torch.cat((input, hidden), 1)
hidden = self.i2h(combined)
output = self.i2o(combined)
output = self.softmax(output)
return output, hidden def initHidden(self):
return torch.zeros(1, self.hidden_size)# 定义属性
n_hidden = 128
rnn = RNN(n_inputs, n_hidden, n_outputs)# 预测函数
def predict(input_line, n_predictions=3):
output, hidden = rnn(Variable(line_tensor), hidden)

topv, topi = output.data.topk(n_predictions, 1, True)
predictions = [] for i in range(n_predictions):
value = topv[0][i]
category_index = topi[0][i]
predictions.append([value, category_index])

return predictions