class_docstr: concat Eigen features
  
  Notes
  -----
  An implementation of [#]_
  
  References
  ----------
  .. [#] Ziwei Zhang, Peng Cui, Jian Pei, Xin Wang, Wenwu Zhu:
      Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs. TKDE (2021)
      https://arxiv.org/abs/2006.04330
  
  
  Parameters
  ----------
  size : int
      EigenGNN hidden size
class_docstr_tok: ["concat","Eigen","features","Notes","--","--","-","An","implementation","of","[","#","]","_","References","--","--","--","--","--","..","[","#","]","Ziwei","Zhang",",","Peng","Cui",",","Jian","Pei",",","Xin","Wang",",","Wenwu","Zhu",":","Eigen-GNN",":","A","Graph","Structure","Preserving","Plug-in","for","GNNs",".","TKDE","(","2021",")","https",":","\/\/arxiv.org\/abs\/2006.04330","Parameters","--","--","--","--","--","size",":","int","EigenGNN","hidden","size"]
code: def _extract_nodes_feature(
      self, data: autogl.data.Data
  ) -> torch.Tensor:
      edge_index: np.ndarray = data.edge_index.numpy()
      edge_weight: np.ndarray = getattr(data, "edge_weight").numpy()
      num_nodes: int = (
          data.x.size(0)
          if data.x is not None and isinstance(data.x, torch.Tensor)
          else (data.edge_index.max().item() + 1)
      )
      adj = csr_matrix(
          (edge_weight, (edge_index[0, :], edge_index[1, :])),
          shape=(num_nodes, num_nodes),
      )
      if np.max(adj - adj.T) > 1e-5:
          adj = adj + adj.T
      mf = _Eigen()
      features: np.ndarray = mf(adj, self.__size)
      return torch.from_numpy(features)
  
file_path: AutoGL/autogl/module/feature/_generators/_eigen.py
imports: import autogl
  import numpy
  from scipy.sparse import csr_matrix
  import scipy.sparse
  import scipy.sparse.linalg
  import networkx
  import torch
  from ._basic import BaseFeatureGenerator
  from .._feature_engineer_registry import FeatureEngineerUniversalRegistry
label_desc: Use image node_id 2 for calling the EigenFeatureGenerator obj's underlying member method code with example usage: obj._extract_nodes_feature(data) and returns: torch