class_docstr: Auto Link Predictor.
  
  Used to automatically solve the link prediction 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``.
docstr: Predict the node class number.
  
  Parameters
  ----------
  dataset: torch_geometric.data.dataset.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``.
  
  mask: str
      The data split to give prediction on. Default ``test``.
  
  threshold: float
      The threshold to judge whether the edges are positive or not.
  
  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",
      threshold=0.5,
  ) -> np.ndarray:
      """
      Predict the node class number.
  
      Parameters
      ----------
      dataset: torch_geometric.data.dataset.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``.
  
      mask: str
          The data split to give prediction on. Default ``test``.
  
      threshold: float
          The threshold to judge whether the edges are positive or not.
  
      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 (proba > threshold).astype("int")
  
imports: import logging
  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.feature import FEATURE_DICT
  from ...module.train import TRAINER_DICT, BaseLinkPredictionTrainer
  from ...module.train import get_feval
  from ..utils import LeaderBoard, get_graph_node_features, convert_dataset, set_seed
  from ...datasets import utils
  from ..utils import get_logger
  from ...backend import DependentBackend
label_desc: Use image node_id 9 for calling the AutoLinkPredictor obj's underlying member method code with example usage: obj.predict(dataset, inplaced, inplace, use_ensemble, use_best, name, mask, threshold) and returns: unknown