给出一个tensor为[85,1139]为输入数据集的pytorch的RNN模型进行训练的代码，数据集随机生成

以下是一个基本的RNN模型，可以用于训练一个输入数据为[85, 1139]的数据集：

import torch
import torch.nn as nn

# 定义超参数
input_size = 1139
hidden_size = 512
num_layers = 2
batch_size = 16
seq_length = 85
num_epochs = 10
learning_rate = 0.001

# 随机生成数据集
data = torch.randn(batch_size, seq_length, input_size)

# 定义模型
class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, 1)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        out, _ = self.rnn(x, h0)
        out = self.fc(out[:, -1, :])
        return out

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = RNN(input_size, hidden_size, num_layers).to(device)

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    for i in range(0, data.size(1) - seq_length, seq_length):
        inputs = data[:, i:i+seq_length, :]
        targets = torch.randn(batch_size, 1)
        inputs = inputs.to(device)
        targets = targets.to(device)

        # 前向传播
        outputs = model(inputs)
        loss = criterion(outputs, targets)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i+1) % 10 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, data.size(1)-seq_length, loss.item()))

这个模型使用了一个两层的RNN，每个RNN层的输出大小为512，使用MSE作为损失函数，使用Adam优化器进行训练。在训练过程中，我们将数据集分成了长度为85的序列，并在每个序列后面添加了一个随机的目标值。