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 __init__(self, meta_model="gbm", meta_params={}, *args, **kwargs):
      super().__init__()
      self.model_name = meta_model.lower()
      assert self.model_name in [
          "gbm",
          "glm",
      ], "Only support gbm and glm when ensemble!"
      self.meta_params = meta_params
  
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 1 to create a new Stacking object from inherited base classes: BaseEnsembler with example: obj = Stacking(meta_model, meta_params)