在一个第一层为GCNmf其他层为GCN的神经网络模型中，在GCNmf层中加入Adaptive node 模块代码

Adaptive node模块是一种能够自适应地调整节点数量的方法，可以在GCNmf层中加入。具体的代码实现可能因框架和模型而异，以下是一种可能的实现方式，供参考：

import torch
import torch.nn as nn
import torch.nn.functional as F

class AdaptiveNodeModule(nn.Module):
    def __init__(self, in_dim, out_dim, max_nodes=1000, eps=1e-6):
        super(AdaptiveNodeModule, self).__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.max_nodes = max_nodes
        self.eps = eps
        
        self.linear1 = nn.Linear(in_dim, out_dim)
        self.linear2 = nn.Linear(out_dim, out_dim)
    
    def forward(self, x, adj):
        # x: [batch_size, num_nodes, in_dim]
        # adj: [batch_size, num_nodes, num_nodes]
        batch_size, num_nodes, _ = x.shape
        
        # Compute node importance scores based on the node features
        node_scores = torch.mean(x, dim=-1)  # [batch_size, num_nodes]
        node_scores = F.relu(node_scores) + self.eps  # [batch_size, num_nodes]
        
        # Select top-k nodes based on node scores
        max_nodes = min(self.max_nodes, num_nodes)
        _, indices = torch.topk(node_scores, max_nodes, dim=-1)  # [batch_size, max_nodes]
        mask = torch.zeros(batch_size, num_nodes, dtype=torch.bool, device=x.device)
        mask.scatter_(-1, indices, True)
        
        # Apply mask to the node features and adjacency matrix
        x = x[mask].view(batch_size, max_nodes, self.in_dim)  # [batch_size, max_nodes, in_dim]
        adj = adj[:, mask, :][:, :, mask]  # [batch_size, max_nodes, max_nodes]
        
        # Apply GCNmf with adaptive node selection
        x = self.linear1(x)
        x = F.relu(x)
        x = self.linear2(x)
        x = torch.matmul(adj, x)
        
        return x, adj

这个模块接收一个大小为`[batch_size, num_nodes, in_dim]`的节点特征矩阵和一个大小为`[batch_size, num_nodes, num_nodes]`的邻接矩阵，然后根据节点特征计算每个节点的重要性得分，并根据得分选择前k个节点进行GCNmf操作。最终输出新的节点特征矩阵和邻接矩阵。