给出一个tensor为[85,1139]为输入数据集的pytorch的RNN代码

以下是一个简单的 PyTorch RNN 模型，用于处理大小为[batch_size, seq_length, input_size]的输入张量，其中batch_size是批量大小，seq_length是序列长度，input_size是输入特征的数量。本例中，我们将输入数据集看作是一个长为85的序列，每个单词用一个长度为1139的向量表示。

import torch
import torch.nn as nn

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

    def forward(self, x, h):
        out, h = self.rnn(x, h)
        out = out[:, -1, :]
        out = self.fc(out)
        return out, h

input_size = 1139
hidden_size = 128
num_layers = 2
output_size = 10

batch_size = 50
seq_length = 85

# 创建一个随机输入张量
input_data = torch.randn(batch_size, seq_length, input_size)

# 创建模型实例
model = RNN(input_size, hidden_size, num_layers, output_size)

# 初始化隐藏状态张量
h0 = torch.zeros(num_layers, batch_size, hidden_size)

# 前向传递
output, h = model(input_data, h0)

print(output.shape)  # 输出形状：(batch_size, output_size)