class_docstr: The node classification trainer.
  
  Used to automatically train the node classification problem.
  
  Parameters
  ----------
  model:
      Models can be ``str``, ``autogl.module.model.BaseAutoModel``, 
      ``autogl.module.model.encoders.BaseEncoderMaintainer`` or a tuple of (encoder, decoder) 
      if need to specify both encoder and decoder. Encoder can be ``str`` or
      ``autogl.module.model.encoders.BaseEncoderMaintainer``, and decoder can be ``str``
      or ``autogl.module.model.decoders.BaseDecoderMaintainer``.
      If only encoder is specified, decoder will be default to "logsoftmax"
  
  num_features: int (Optional)
      The number of features in dataset. default None
  
  num_classes: int (Optional)
      The number of classes. default None
  
  optimizer: ``Optimizer`` of ``str``
      The (name of) optimizer used to train and predict. default torch.optim.Adam
  
  lr: ``float``
      The learning rate of node classification task. default 1e-4
  
  max_epoch: ``int``
      The max number of epochs in training. default 100
  
  early_stopping_round: ``int``
      The round of early stop. default 100
  
  weight_decay: ``float``
      weight decay ratio, default 1e-4
  
  device: ``torch.device`` or ``str``
      The device where model will be running on.
  
  init: ``bool``
      If True(False), the model will (not) be initialized.
  
  feval: (Sequence of) ``Evaluation`` or ``str``
      The evaluation functions, default ``[LogLoss]``
  
  loss: ``str``
      The loss used. Default ``nll_loss``.
  
  lr_scheduler_type: ``str`` (Optional)
      The lr scheduler type used. Default None.
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docstr: Predict on the given dataset using specified mask.
  
  Parameters
  ----------
  dataset: The node classification dataset used to be predicted.
  
  mask: ``train``, ``val``, or ``test``.
      The dataset mask.
  
  Returns
  -------
  The prediction result.
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code: def predict(self, dataset, mask=None):
      """
      Predict on the given dataset using specified mask.
  
      Parameters
      ----------
      dataset: The node classification dataset used to be predicted.
  
      mask: ``train``, ``val``, or ``test``.
          The dataset mask.
  
      Returns
      -------
      The prediction result.
      """
      return self.predict_proba(
          dataset, mask=mask, in_log_format=True
      ).max(1)[1]
  
imports: from .None import register_trainer
  from .base import BaseNodeClassificationTrainer, EarlyStopping
  import torch
  from torch.optim.lr_scheduler import StepLR, MultiStepLR, ExponentialLR, ReduceLROnPlateau
  import torch.nn.functional
  from ..model import BaseEncoderMaintainer, BaseDecoderMaintainer, BaseAutoModel
  from .evaluation import Evaluation, get_feval, Logloss
  from typing import Callable, Iterable, Optional, Tuple, Type, Union
  from copy import deepcopy
  from ...utils import get_logger
  from ...backend import DependentBackend
label_desc: Use image node_id 6 for calling the NodeClassificationFullTrainer obj's underlying member method code with example usage: obj.predict(dataset, mask) and returns: self