AI写代码修Bug画画写诗，ChatGPT&DALLE2试用攻略

import torch
import torch.nn as nn

class GraphSAGE(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, num_samples):
        super(GraphSAGE, self).__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.num_samples = num_samples

        self.embedding = nn.Embedding(input_dim, hidden_dim)
        self.aggregator = nn.Linear(hidden_dim * num_samples, hidden_dim)
        self.classifier = nn.Linear(hidden_dim, output_dim)

    def forward(self, nodes, neighbors):
        # Extract node embeddings
        node_embeddings = self.embedding(nodes)

        # Sample and aggregate neighbor embeddings
        neighbor_embeddings = [self.embedding(neighbors[:, i])
                               for i in range(self.num_samples)]
        neighbor_embeddings = torch.cat(neighbor_embeddings, dim=1)
        aggregated_neighbors = self.aggregator(neighbor_embeddings)

        # Combine node and aggregated neighbor embeddings
        combined = torch.cat([node_embeddings, aggregated_neighbors], dim=1)

        # Predict node labels
        return self.classifier(combined)