class_docstr: Auto Multi-class Graph Classifier.
  
  Used to automatically solve the graph 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.
      Disable feature engineer by setting it to ``None``. Default ``deepgl``.
  
  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.
      Disable hpo by setting it to ``None``. Default ``anneal``.
  
  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``. Default ``voting``.
  
  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: Iterable[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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docstr: Predict the node class number.
  
  Parameters
  ----------
  dataset: autogl.data.Dataset or None
      The dataset needed to predict. If ``None``, will use the processed dataset passed
      to ``fit()`` instead. Default ``None``.
  
  inplaced: bool
      Whether the given dataset is processed. Only be effective when ``dataset``
      is not ``None``. If you pass the dataset to ``fit()`` with ``inplace=True``, and
      you pass the dataset again to this method, you should set this argument to ``True``.
      Otherwise ``False``. Default ``False``.
  
  inplace: bool
      Whether we process the given dataset in inplace manner. Default ``False``.
      Set it to True if you want to save memory by modifying the given dataset directly.
  
  use_ensemble: bool
      Whether to use ensemble to do the predict. Default ``True``.
  
  use_best: bool
      Whether to use the best single model to do the predict. Will only be effective
      when ``use_ensemble`` is ``False``. Default ``True``.
  
  name: str or None
      The name of model used to predict. Will only be effective when ``use_ensemble``
      and ``use_best`` both are ``False``. Default ``None``.
  
  Returns
  -------
  result: np.ndarray
      An array of shape ``(N,)``, where ``N`` is the number of test nodes.
      The prediction on given dataset.
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code: def predict(
      self,
      dataset=None,
      inplaced=False,
      inplace=False,
      use_ensemble=True,
      use_best=True,
      name=None,
      mask="test",
  ) -> np.ndarray:
      """
      Predict the node class number.
  
      Parameters
      ----------
      dataset: autogl.data.Dataset or None
          The dataset needed to predict. If ``None``, will use the processed dataset passed
          to ``fit()`` instead. Default ``None``.
  
      inplaced: bool
          Whether the given dataset is processed. Only be effective when ``dataset``
          is not ``None``. If you pass the dataset to ``fit()`` with ``inplace=True``, and
          you pass the dataset again to this method, you should set this argument to ``True``.
          Otherwise ``False``. Default ``False``.
  
      inplace: bool
          Whether we process the given dataset in inplace manner. Default ``False``.
          Set it to True if you want to save memory by modifying the given dataset directly.
  
      use_ensemble: bool
          Whether to use ensemble to do the predict. Default ``True``.
  
      use_best: bool
          Whether to use the best single model to do the predict. Will only be effective
          when ``use_ensemble`` is ``False``. Default ``True``.
  
      name: str or None
          The name of model used to predict. Will only be effective when ``use_ensemble``
          and ``use_best`` both are ``False``. Default ``None``.
  
      Returns
      -------
      result: np.ndarray
          An array of shape ``(N,)``, where ``N`` is the number of test nodes.
          The prediction on given dataset.
      """
      proba = self.predict_proba(
          dataset, inplaced, inplace, use_ensemble, use_best, name, mask
      )
      return np.argmax(proba, axis=1)
  
imports: import time
  import json
  from copy import deepcopy
  from typing import Sequence
  import torch
  import numpy
  import yaml
  from .utils import _parse_hp_space, _parse_model_hp, _initialize_single_model
  from ..base import BaseClassifier
  from ....module.model import EncoderUniversalRegistry, DecoderUniversalRegistry, ModelUniversalRegistry
  from ....module.feature import FEATURE_DICT
  from ....module.train import TRAINER_DICT, get_feval, BaseGraphClassificationTrainer
  from ....module.train.ssl import BaseContrastiveTrainer
  from ...utils import LeaderBoard, get_dataset_labels, set_seed, get_graph_from_dataset, get_graph_node_features, convert_dataset
  from ....datasets import utils
  from ...utils import get_logger
  from ....backend import DependentBackend
label_desc: Use image node_id 7 for calling the SSLGraphClassifier obj's underlying member method code with example usage: obj.predict(dataset, inplaced, inplace, use_ensemble, use_best, name, mask) and returns: np