combined_test_set = torch.cat((time_test_set, features_test_set_normalized_tensor), dim=1)

这行代码将会把张量 `time_test_set` 和 `features_test_set_normalized_tensor` 沿着维度1进行拼接。在PyTorch中，`torch.cat()` 函数的第一个参数是一个元组(tuple)，包含需要拼接的张量。第二个参数 `dim` 表示需要拼接的维度。在这里，我们将 `dim` 设置为1，即沿着第二个维度进行拼接。假设 `time_test_set` 的维度为 `(batch_size, seq_length, input_dim1)`，`features_test_set_normalized_tensor` 的维度为 `(batch_size, seq_length, input_dim2)`，那么拼接后的张量 `combined_test_set` 的维度为 `(batch_size, seq_length, input_dim1+input_dim2)`。

相关问题

combined_node_feat = torch.cat((old_node_feat, new_node_feat), dim=1)

这行代码使用 PyTorch 中的 `torch.cat` 函数将两个张量 `old_node_feat` 和 `new_node_feat` 沿着第二个维度（即列）拼接在一起，生成一个新的张量 `combined_node_feat`。

假设 `old_node_feat` 的形状为 (n, m1)，`new_node_feat` 的形状为 (n, m2)，那么 `combined_node_feat` 的形状将会是 (n, m1+m2)。

具体实现可以看下面的示例代码：

import torch

# 假设 old_node_feat 和 new_node_feat 是两个张量
old_node_feat = torch.randn(10, 5)  # 形状为 (10, 5) 的张量
new_node_feat = torch.randn(10, 3)  # 形状为 (10, 3) 的张量

# 使用 torch.cat 将两个张量拼接在一起
combined_node_feat = torch.cat((old_node_feat, new_node_feat), dim=1)  # 沿着第二个维度拼接

# 打印出新的张量的形状
print(combined_node_feat.shape)  # 输出 (10, 8)

这里将 `old_node_feat` 和 `new_node_feat` 沿着第二个维度拼接在一起，生成了一个形状为 (10, 8) 的新张量 `combined_node_feat`。

把这段代码里的location_embedding_dim去掉class my_GAT(torch.nn.Module): def __init__(self, in_channels, out_channels, n_heads, location_embedding_dim, filters_1, filters_2, dropout): super(my_GAT, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.n_heads = n_heads location_embedding_dim = 0 self.filters_1 = filters_1 self.filters_2 = filters_2 self.dropout = dropout self.location_embedding_dim = location_embedding_dim self.setup_layers() def setup_layers(self): self.GAT_1 = GATConv(in_channels=self.in_channels,out_channels=self.filters_1, heads=self.n_heads, dropout=0.1) self.GAT_2 = GATConv(in_channels=self.filters_1 * self.n_heads + self.location_embedding_dim, out_channels=self.out_channels, heads=self.n_heads, dropout=0.1, concat=False) def forward(self, edge_indices, features, location_embedding): features = torch.cat((features, location_embedding), dim=-1) features = self.GAT_1(features, edge_indices) features = torch.nn.functional.relu(features) features = torch.nn.functional.dropout(features, p=self.dropout, training=self.training) features = torch.cat((features, location_embedding), dim=-1) features = self.GAT_2(features, edge_indices) return features

这段代码里的location_embedding_dim是一个参数，它表示位置嵌入的维度。如果你想去掉它，可以把它的值设为0，然后在构造函数中把self.location_embedding_dim也设为0即可。具体来说，修改后的代码如下：

class my_GAT(torch.nn.Module):
    def __init__(self, in_channels, out_channels, n_heads, filters_1, filters_2, dropout):
        super(my_GAT, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.n_heads = n_heads
        self.filters_1 = filters_1
        self.filters_2 = filters_2
        self.dropout = dropout
        self.location_embedding_dim = 0
        self.setup_layers()
        
    def setup_layers(self):
        self.GAT_1 = GATConv(in_channels=self.in_channels,out_channels=self.filters_1, heads=self.n_heads, dropout=0.1)
        self.GAT_2 = GATConv(in_channels=self.filters_1 * self.n_heads, out_channels=self.out_channels, heads=self.n_heads, dropout=0.1, concat=False)
         
    def forward(self, edge_indices, features, location_embedding):
        features = self.GAT_1(features, edge_indices)
        features = torch.nn.functional.relu(features)
        features = torch.nn.functional.dropout(features, p=self.dropout, training=self.training)
        features = self.GAT_2(features, edge_indices)
        return features