class_docstr: Notes
  -----
  An implementation of auto feature engineering method Deepgl [#]_ ,which iteratively generates features by aggregating neighbour features
  and select a fixed number of  features to automatically add important graph-aware features.
  References
  ----------
  .. [#] Rossi, R. A., Zhou, R., & Ahmed, N. K. (2020).
      Deep Inductive Graph Representation Learning.
      IEEE Transactions on Knowledge and Data Engineering, 32(3), 438–452.
      https://doi.org/10.1109/TKDE.2018.2878247
  Parameters
  ----------
  fixlen : int
      fixed number of features for every epoch. The final number of features added will be
      ``fixlen`` \times ``max_epoch``, 200 \times 5 in default.
  max_epoch : int
      number of epochs in total process.
  timebudget : int
      timebudget(seconds) for the feature engineering process, None for no time budget . Note that
      this time budget is a soft budget ,which is obtained by rough time estimation through previous iterations and
      may finally exceed the actual timebudget
  y_sel_func : Callable
      feature selector function object for selection at each iteration ,lightgbm in default. Note that in original paper,
      connected components of feature graph is used , and you may implement it by yourself if you want.
  verbosity : int
      hide any infomation except error and fatal if ``verbosity`` < 1
code: def _transform(self, data):
      x = data.x
      gx = x.copy()
      for sel in self._sel:
          gx = self._gen(gx)
          gx = scale(gx)
          gx = gx[:, sel]
          x = np.concatenate([x, gx], axis=1)
      data.x = x
      return data
  
imports: from sklearn import preprocessing
  import numpy
  from sklearn.metrics.pairwise import cosine_similarity
  import copy
  from tqdm import tqdm
  from tabulate import tabulate
  import time
  from .base import BaseFeature, BaseFeatureEngineer
  from .selectors import SeGBDT
  from .None import register_feature
  from ...utils import get_logger
  import torch
label_desc: Use image node_id 5 for calling the AutoFeatureEngineer obj's underlying member method code with example usage: obj._transform(data) and returns: data