code: def test_trim_sparse_tensor():
      edge_index = torch.tensor([[0, 0, 1, 2], [1, 2, 3, 4]])
      adj = SparseTensor.from_edge_index(
          edge_index, sparse_sizes=[5, 5]
      )
  
      adj = trim_sparse_tensor(adj, size=(3, 3), num_seed_nodes=1)
  
      row, col, _ = adj.coo()
      assert row.tolist() == [0, 0]
      assert col.tolist() == [1, 2]
  
imports: from typing import List, Optional
  import torch
  from torch import Tensor
  import torch_geometric.typing
  from torch_geometric.data import Data
  from torch_geometric.loader import NeighborLoader
  from torch_geometric.nn import GraphConv
  from torch_geometric.testing import withPackage
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import trim_to_layer
  from torch_geometric.utils._trim_to_layer import trim_sparse_tensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_trim_sparse_tensor() without return types