class_docstr: A stacking ensembler. Currently we support gradient boosting as the meta-algorithm.
  
  Parameters
  ----------
  meta_model : 'gbm' or 'glm' (Optional)
      Type of the stacker:
          'gbm' : Gradient boosting model. This is the default.
          'glm' : Generalized linear model.
  
  meta_params : a ``dict``  (Optional)
      When ``meta_model`` is specified, you can customize the parameters of the stacker.
      If this argument is not provided, the stacker will be configurated with default parameters.
      Default ``{}``.
code: def _re_initialize(self, identifiers, n_models):
      self.identifiers = identifiers
      self.model = None
  
file_path: AutoGL/autogl/module/ensemble/stacking.py
imports: import numpy
  import torch
  import torch.nn.functional
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.linear_model import LogisticRegression
  from .base import BaseEnsembler
  from .None import register_ensembler
  from ...utils import get_logger
label_desc: Use image node_id 4 for calling the Stacking obj's underlying member method code with example usage: obj._re_initialize(identifiers, n_models) without return types