pytorch: GRU无法就地更新hidden_state

import torch
import torch.nn as nn

# 定义GRU模型
class GRUModel(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(GRUModel, self).__init__()
        self.gru = nn.GRU(input_size, hidden_size, batch_first=True)

    def forward(self, x, hidden_state):
        output, new_hidden_state = self.gru(x, hidden_state)
        return output, new_hidden_state

# 创建GRU模型实例
input_size = 10
hidden_size = 20
model = GRUModel(input_size, hidden_size)

# 定义输入数据和初始hidden_state
batch_size = 32
sequence_length = 5
x = torch.randn(batch_size, sequence_length, input_size)
hidden_state = torch.zeros(1, batch_size, hidden_size)  # 初始hidden_state

# 前向传播
output, new_hidden_state = model(x, hidden_state)

# 输出结果
print(output.shape)  # 输出维度：(batch_size, sequence_length, hidden_size)
print(new_hidden_state.shape)  # 输出维度：(1, batch_size, hidden_size)