class_docstr: Base solver class, define some standard solver interfaces.
  
  Parameters
  ----------
  feature_module: autogl.module.feature.BaseFeatureEngineer or str or None
      The (name of) auto feature engineer used to process the given dataset.
      Disable feature engineer by setting it to ``None``.
  
  graph_models: list of autogl.module.model.BaseModel or str
      The (name of) models to be optimized as backbone.
  
  hpo_module: autogl.module.hpo.BaseHPOptimizer or str or None
      The (name of) hpo module used to search for best hyper parameters.
      Disable hpo by setting it to ``None``.
  
  ensemble_module: autogl.module.ensemble.BaseEnsembler or str or None
      The (name of) ensemble module used to ensemble the multi-models found.
      Disable ensemble by setting it to ``None``.
  
  max_evals: int (Optional)
      If given, will set the number eval times the hpo module will use.
      Only be effective when hpo_module is  of type ``str``. Default ``50``.
  
  default_trainer: str or list of str (Optional)
      Default trainer class to be used.
      If a single trainer class is given, will set all trainer to default trainer.
      If a list of trainer class is given, will set every model with corresponding trainer
      cls. Default ``None``.
  
  trainer_hp_space: list of dict (Optional)
      trainer hp space or list of trainer hp spaces configuration.
      If a single trainer hp is given, will specify the hp space of trainer for every model.
      If a list of trainer hp is given, will specify every model with corrsponding
      trainer hp space. Default ``None``
  
  model_hp_spaces: list of list of dict (Optional)
      model hp space configuration.
      If given, will specify every hp space of every passed model. Default ``None``.
  
  size: int (Optional)
      The max models ensemble module will use. Default ``None``.
  
  device: torch.device or str
      The device where model will be running on. If set to ``auto``, will use gpu when available.
      You can also specify the device by directly giving ``gpu`` or ``cuda:0``, etc.
      Default ``auto``.
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docstr: Set the graph models used in current solver.
  
  Parameters
  ----------
  graph_models: list of autogl.module.model.BaseModel or list of str
      The (name of) models to be optimized as backbone.
  
  default_trainer: str or list of str (Optional)
      Default trainer class to be used.
      If a single trainer class is given, will set all trainer to default trainer.
      If a list of trainer class is given, will set every model with corresponding trainer
      cls. Default ``None``.
  
  trainer_hp_space: list of dict (Optional)
      trainer hp space or list of trainer hp spaces configuration.
      If a single trainer hp is given, will specify the hp space of trainer for every model.
      If a list of trainer hp is given, will specify every model with corrsponding
      trainer hp space.
      Default ``None``.
  
  model_hp_spaces: list of list of dict (Optional)
      model hp space configuration.
      If given, will specify every hp space of every passed model. Default ``None``.
  
  Returns
  -------
  self: autogl.solver.BaseSolver
      A reference of current solver.
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code: def set_graph_models(
      self,
      graph_models,
      default_trainer=None,
      trainer_hp_space=None,
      model_hp_spaces=None,
  ) -> "BaseSolver":
      r"""
      Set the graph models used in current solver.
  
      Parameters
      ----------
      graph_models: list of autogl.module.model.BaseModel or list of str
          The (name of) models to be optimized as backbone.
  
      default_trainer: str or list of str (Optional)
          Default trainer class to be used.
          If a single trainer class is given, will set all trainer to default trainer.
          If a list of trainer class is given, will set every model with corresponding trainer
          cls. Default ``None``.
  
      trainer_hp_space: list of dict (Optional)
          trainer hp space or list of trainer hp spaces configuration.
          If a single trainer hp is given, will specify the hp space of trainer for every model.
          If a list of trainer hp is given, will specify every model with corrsponding
          trainer hp space.
          Default ``None``.
  
      model_hp_spaces: list of list of dict (Optional)
          model hp space configuration.
          If given, will specify every hp space of every passed model. Default ``None``.
  
      Returns
      -------
      self: autogl.solver.BaseSolver
          A reference of current solver.
      """
      self.gml = graph_models
      self._default_trainer = default_trainer
      self._trainer_hp_space = trainer_hp_space
      self._model_hp_spaces = model_hp_spaces
      return self
  
imports: from typing import Any, Iterable, Tuple
  from copy import deepcopy
  import torch
  from ..module.feature import FEATURE_DICT
  from ..module.hpo import HPO_DICT
  from ..module.model import EncoderUniversalRegistry, DecoderUniversalRegistry, ModelUniversalRegistry
  from ..module.nas.algorithm import NAS_ALGO_DICT
  from ..module.nas.estimator import NAS_ESTIMATOR_DICT
  from ..module.nas.space import NAS_SPACE_DICT
  from ..module import BaseFeatureEngineer, BaseHPOptimizer, BaseTrainer
  from .utils import LeaderBoard
  from ..utils import get_logger
label_desc: Use image node_id 3 for calling the BaseSolver obj's underlying member method code with example usage: obj.set_graph_models(graph_models, default_trainer, trainer_hp_space, model_hp_spaces) and returns: self