code: def test_meta_layer():
      assert str(MetaLayer()) == (
          "MetaLayer(\n"
          "  edge_model=None,\n"
          "  node_model=None,\n"
          "  global_model=None\n"
          ")"
      )
  
      def dummy_model(*args):
          global count
          count += 1
          return None
  
      x = torch.randn(20, 10)
      edge_index = torch.randint(
          0, high=10, size=(2, 20), dtype=torch.long
      )
  
      for edge_model in (dummy_model, None):
          for node_model in (dummy_model, None):
              for global_model in (dummy_model, None):
                  model = MetaLayer(
                      edge_model, node_model, global_model
                  )
                  out = model(x, edge_index)
                  assert isinstance(out, tuple) and len(out) == 3
  
      assert count == 12
  
file_path: pytorch_geometric/test/nn/models/test_meta.py
imports: from typing import Optional
  import torch
  from torch import Tensor
  from torch.nn import Linear
  from torch.nn import ReLU
  from torch.nn import Sequential
  from torch_geometric.nn import MetaLayer
  from torch_geometric.testing import is_full_test
  from torch_geometric.utils import scatter
label_desc: Use image node_id 1 for calling a global function with example usage: test_meta_layer() and returns: None