code: def eigenvector_centrality(data):
      graph = to_networkx(data)
      x = nx.eigenvector_centrality_numpy(graph)
      x = [x[i] for i in range(data.num_nodes)]
      return torch.tensor(x, dtype=torch.float32).to(
          data.edge_index.device
      )
  
file_path: DIG/dig/sslgraph/utils/adaptive.py
imports: import torch
  from torch.utils.data import random_split
  from torch_geometric.utils import degree, remove_self_loops, get_laplacian, to_undirected, to_dense_adj, to_networkx, degree, to_undirected
  from torch_scatter import scatter
  import networkx
label_desc: Use image node_id 2 for calling a global function with example usage: eigenvector_centrality(data) and returns: torch