code: def test_from_networkx_set_node_attributes():
      import networkx as nx
  
      G = nx.path_graph(3)
      attrs = {
          0: {"x": torch.tensor([1, 0, 0])},
          1: {"x": torch.tensor([0, 1, 0])},
          2: {"x": torch.tensor([0, 0, 1])},
      }
      nx.set_node_attributes(G, attrs)
  
      assert from_networkx(G).x.tolist() == [
          [1, 0, 0],
          [0, 1, 0],
          [0, 0, 1],
      ]
  
imports: import pytest
  import scipy.sparse
  import torch
  from torch_geometric.data import Data, HeteroData
  from torch_geometric.testing import get_random_edge_index, withPackage
  from torch_geometric.utils import from_cugraph, from_dgl, from_networkit, from_networkx, from_scipy_sparse_matrix, from_trimesh, sort_edge_index, subgraph, to_cugraph, to_dgl, to_networkit, to_networkx, to_scipy_sparse_matrix, to_trimesh
label_desc: Use image node_id 4 for calling a global function with example usage: test_from_networkx_set_node_attributes() without return types