code: def test_mogonet_gcn_conv_output(test_data):
      in_channels = 2
      out_channels = 8
      num_samples = 3
      conv = MogonetGCNConv(
          in_channels=in_channels, out_channels=out_channels
      )
  
      # Test the forward pass
      x = test_data.x
      adj_t = test_data.adj_t
      output = conv(x, adj_t)
      assert output.shape == (num_samples, out_channels)
  
      # Test the message function
      x_j = torch.randn(3, 5)
      msg = conv.message(x_j)
      assert msg.shape == (3, 5)
  
      # Test the message_and_aggregate function
      agg = conv.message_and_aggregate(adj_t, x)
      assert agg.shape == (num_samples, in_channels)
  
      # Test the update function
      aggr_out = torch.randn(num_samples, out_channels)
      updated = conv.update(aggr_out)
      assert updated.shape == aggr_out.shape
  
file_path: pykale/tests/embed/test_mogonet.py
imports: import pytest
  import torch
  from torch_geometric.data import Data
  from torch_sparse import SparseTensor
  from kale.embed.mogonet import MogonetGCN, MogonetGCNConv
label_desc: Use image node_id 5 for calling a global function with example usage: test_mogonet_gcn_conv_output(test_data) without return types