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: Fit current solver on given dataset.
  
  Parameters
  ----------
  dataset: autogl.data.dataset
      The multi-graph dataset needed to fit on.
  
  time_limit: int
      The time limit of the whole fit process (in seconds). If set below 0, will ignore
      time limit. Default ``-1``.
  
  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.
  
  train_split: float or int (Optional)
      The train ratio (in ``float``) or number (in ``int``) of dataset. If you want to use
      default train/val/test split in dataset, please set this to ``None``.
      Default ``None``.
  
  val_split: float or int (Optional)
      The validation ratio (in ``float``) or number (in ``int``) of dataset. If you want to
      use default train/val/test split in dataset, please set this to ``None``.
      Default ``None``.
  
  evaluation_method: (list of) str autogl.module.train.evaluation
      A (list of) evaluation method for current solver. If ``infer``, will automatically
      determine. Default ``infer``.
  
  seed: int (Optional)
      The random seed. If set to ``None``, will run everything at random.
      Default ``None``.
  
  Returns
  -------
  self: autogl.solver.AutoGraphClassifier
      A reference of current solver.
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code: def fit(
      self,
      dataset,
      time_limit=-1,
      inplace=False,
      train_split=None,
      val_split=None,
      evaluation_method="infer",
      seed=None,
  ) -> "AutoGraphClassifier":
      """
      Fit current solver on given dataset.
  
      Parameters
      ----------
      dataset: autogl.data.dataset
          The multi-graph dataset needed to fit on.
  
      time_limit: int
          The time limit of the whole fit process (in seconds). If set below 0, will ignore
          time limit. Default ``-1``.
  
      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.
  
      train_split: float or int (Optional)
          The train ratio (in ``float``) or number (in ``int``) of dataset. If you want to use
          default train/val/test split in dataset, please set this to ``None``.
          Default ``None``.
  
      val_split: float or int (Optional)
          The validation ratio (in ``float``) or number (in ``int``) of dataset. If you want to
          use default train/val/test split in dataset, please set this to ``None``.
          Default ``None``.
  
      evaluation_method: (list of) str autogl.module.train.evaluation
          A (list of) evaluation method for current solver. If ``infer``, will automatically
          determine. Default ``infer``.
  
      seed: int (Optional)
          The random seed. If set to ``None``, will run everything at random.
          Default ``None``.
  
      Returns
      -------
      self: autogl.solver.AutoGraphClassifier
          A reference of current solver.
      """
  
      set_seed(seed)
  
      num_classes = get_dataset_labels(dataset).max().item() + 1
  
      if time_limit < 0:
          time_limit = 3600 * 24
      time_begin = time.time()
  
      # initialize leaderboard
      if evaluation_method == "infer":
          if hasattr(dataset, "metric"):
              evaluation_method = [dataset.metric]
          else:
              if num_classes == 2:
                  evaluation_method = ["auc"]
              else:
                  evaluation_method = ["acc"]
      assert isinstance(evaluation_method, list)
      evaluator_list = get_feval(evaluation_method)
  
      self.leaderboard = LeaderBoard(
          [e.get_eval_name() for e in evaluator_list],
          {
              e.get_eval_name(): e.is_higher_better()
              for e in evaluator_list
          },
      )
  
      # set up the dataset
      if train_split is None and val_split is None:
          assert hasattr(dataset, "train_split") and hasattr(
              dataset, "val_split"
          ), (
              "The dataset has no default train/val split! "
              "Please manually pass train and val ratio."
          )
          LOGGER.info(
              "Use the default train/val/test ratio in given dataset"
          )
          # if hasattr(dataset.train_split, "n_splits"):
          #    cross_validation = True
  
      elif train_split is not None and val_split is not None:
          utils.graph_random_splits(
              dataset, train_split, val_split, seed=seed
          )
      else:
          LOGGER.error(
              "Please set both train_split and val_split explicitly. Detect %s is None.",
              "train_split" if train_split is None else "val_split",
          )
          raise ValueError(
              "In consistent setting of train/val split. Detect {} is None.".format(
                  "train_split" if train_split is None else "val_split"
              )
          )
  
      # feature engineering
      if self.feature_module is not None:
          self.feature_module.fit(dataset.train_split)
          dataset = self.feature_module.transform(
              dataset, inplace=inplace
          )
  
      self.dataset = dataset
  
      # check whether the dataset has features.
      # currently we only support graph classification with features.
  
      feat = get_graph_node_features(get_graph_from_dataset(dataset))
      assert feat is not None, (
          "Does not support fit on non node-feature dataset!"
          " Please add node features to dataset or specify feature engineers that generate"
          " node features."
      )
      num_features = feat.size(-1)
  
      # initialize graph networks
      self._init_graph_module(
          self.gml,
          num_features=num_features,
          num_classes=num_classes,
          feval=evaluator_list,
          device=self.runtime_device,
          loss="NT_Xent"
          if not hasattr(dataset, "loss")
          else dataset.loss,
          num_graph_features=(
              0
              if not hasattr(dataset[0], "gf")
              else dataset[0].gf.size(1)
          )
          if BACKEND == "pyg"
          else (
              0
              if "gf" not in dataset[0].data
              else dataset[0].data["gf"].size(1)
          ),
      )
  
      # train the models and tune hpo
      result_valid = []
      names = []
      for idx, model in enumerate(self.graph_model_list):
          if time_limit < 0:
              time_for_each_model = None
          else:
              time_for_each_model = (
                  time_limit - time.time() + time_begin
              ) / (len(self.graph_model_list) - idx)
          if self.hpo_module is None:
              model.initialize()
              model.train(convert_dataset(dataset), True)
              optimized = model
          else:
              optimized, _ = self.hpo_module.optimize(
                  trainer=model,
                  dataset=convert_dataset(dataset),
                  time_limit=time_for_each_model,
              )
          # to save memory, all the trainer derived will be mapped to cpu
          optimized.to(torch.device("cpu"))
          name = str(optimized) + "_idx%d" % (idx)
          names.append(name)
          performance_on_valid, _ = optimized.get_valid_score(
              return_major=False
          )
          result_valid.append(
              optimized.get_valid_predict_proba().detach().cpu().numpy()
          )
          self.leaderboard.insert_model_performance(
              name,
              dict(
                  zip(
                      [e.get_eval_name() for e in evaluator_list],
                      performance_on_valid,
                  )
              ),
          )
          self.trained_models[name] = optimized
  
      # fit the ensemble model
      if self.ensemble_module is not None:
          performance = self.ensemble_module.fit(
              result_valid,
              get_dataset_labels(dataset)[dataset.val_index]
              .cpu()
              .numpy(),
              names,
              evaluator_list,
              n_classes=num_classes,
          )
          self.leaderboard.insert_model_performance(
              "ensemble",
              dict(
                  zip(
                      [e.get_eval_name() for e in evaluator_list],
                      performance,
                  )
              ),
          )
  
      return self
  
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 3 for calling the SSLGraphClassifier obj's underlying member method code with example usage: obj.fit(dataset, time_limit, inplace, train_split, val_split, evaluation_method, seed) and returns: self