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 __init__(
      self,
      fixlen=200,
      max_epoch=5,
      timebudget=None,
      y_sel_func=SeGBDT,
      verbosity=-1,
      *args,
      **kwargs,
  ):
      super(AutoFeatureEngineer, self).__init__(
          multigraph=False, *args, **kwargs
      )
      self._ops = [op_sum, op_mean, op_max, op_min]
      self._sim = cos_sim
      self._fixlen = fixlen
      self._max_epoch = max_epoch
      self._timebudget = timebudget
      self._y_sel_func = y_sel_func(
          fixlen, verbose_eval=verbosity >= 1, *args, **kwargs
      )
      self._verbosity = verbosity
  
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 1 to create a new AutoFeatureEngineer object from inherited base classes: BaseFeatureEngineer with example: obj = AutoFeatureEngineer(fixlen, max_epoch, timebudget, y_sel_func, verbosity)