class_docstr: Auto Multi-class HeteroGraph Node Classifier.
  
  Used to automatically solve the heterogeneous node classification problems.
  
  Parameters
  ----------
  feature_module: autogl.module.feature.BaseFeatureEngineer or str or None
      The (name of) auto feature engineer used to process the given dataset. Default ``deepgl``.
      Disable feature engineer by setting it to ``None``.
  
  graph_models: list of autogl.module.model.BaseModel or list of str
      The (name of) models to be optimized as backbone. Default ``['gat', 'gcn']``.
  
  hpo_module: autogl.module.hpo.BaseHPOptimizer or str or None
      The (name of) hpo module used to search for best hyper parameters. Default ``anneal``.
      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. Default ``voting``.
      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 ``str``. Default ``None``.
  
  default_trainer: str (Optional)
      The (name of) the trainer used in this solver. Default to ``NodeClassificationFull``.
  
  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``.
      If the encoder(-decoder) is passed, the space should be a dict containing keys "encoder"
      and "decoder", specifying the detailed encoder decoder hp spaces.
  
  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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code: def __init__(
      self,
      graph_models=("han", "hgt"),
      hpo_module="anneal",
      ensemble_module="voting",
      max_evals=50,
      default_trainer="NodeClassificationHet",
      trainer_hp_space=None,
      model_hp_spaces=None,
      size=4,
      device="auto",
  ):
      super().__init__(
          # currently we do not support feature engineering
          feature_module=None,
          graph_models=graph_models,
          # currently we do not support nas for heterogeneous node classifier
          nas_algorithms=None,
          nas_spaces=None,
          nas_estimators=None,
          hpo_module=hpo_module,
          ensemble_module=ensemble_module,
          max_evals=max_evals,
          default_trainer=default_trainer,
          trainer_hp_space=trainer_hp_space,
          model_hp_spaces=model_hp_spaces,
          size=size,
          device=device,
      )
  
      # data to be kept when fit
      self.dataset = None
  
imports: import time
  import json
  from copy import deepcopy
  from typing import Sequence
  import torch
  import numpy
  import yaml
  from ..base import BaseClassifier
  from ...base import _parse_hp_space, _initialize_single_model, _parse_model_hp
  from ....module.train import TRAINER_DICT, BaseNodeClassificationHetTrainer
  from ....module.train import get_feval
  from ...utils import LeaderBoard, set_seed
  from ....utils import get_logger
label_desc: Use image node_id 1 to create a new AutoHeteroNodeClassifier object from inherited base classes: BaseClassifier with example: obj = AutoHeteroNodeClassifier(graph_models, hpo_module, ensemble_module, max_evals, default_trainer, trainer_hp_space, model_hp_spaces, size, device)