在这种情况下如何计算“映射张量”？

Question

我正在这里浏览PyTorch Geometric文档：https://pytorch-geometric.readthedocs.io/en/latest/notes/create_gnn.html

他们解释了一个“基本”代码：

import torch
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import add_self_loops, degree

class GCNConv(MessagePassing):
def __init__(self, in_channels, out_channels):
    super(GCNConv, self).__init__(aggr='add')  # "Add" aggregation.
    self.lin = torch.nn.Linear(in_channels, out_channels)

def forward(self, x, edge_index):
    # x has shape [N, in_channels]
    # edge_index has shape [2, E]

    # Step 1: Add self-loops to the adjacency matrix.
    edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))

    # Step 2: Linearly transform node feature matrix.
    x = self.lin(x)

    # Step 3: Compute normalization
    row, col = edge_index
    deg = degree(row, x.size(0), dtype=x.dtype)
    deg_inv_sqrt = deg.pow(-0.5)
    norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]

    # Step 4-6: Start propagating messages.
    return self.propagate(edge_index, size=(x.size(0), x.size(0)), x=x,
                          norm=norm)

def message(self, x_j, norm):
    # x_j has shape [E, out_channels]

    # Step 4: Normalize node features.
    return norm.view(-1, 1) * x_j

def update(self, aggr_out):
    # aggr_out has shape [N, out_channels]

    # Step 6: Return new node embeddings.
    return aggr_out

说明包含以下段落：

“在message（）函数中，我们需要归一化相邻节点特征x_j。在这里，x_j表示映射张量，其中包含每个边的相邻节点特征。可以通过附加_i或_j来自动映射节点特征实际上，任何张量都可以通过这种方式映射，只要它们的第一维中有?个条目即可。”

问题：我知道此映射张量x_j是一个张量，其中包含每个边缘的相邻节点特征。他们如何计算x_j？这是否意味着如果将x的名称更改为x_i，我将自动具有“映射张量”？