class_docstr: The heterogeneous node classification trainer.
  
  Parameters
  ----------
  model: ``autogl.module.model.BaseAutoModel``
      Currently Heterogeneous trainer doesn't support decoupled model setting.
  
  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.
class_docstr_tok: ["The","heterogeneous","node","classification","trainer",".","Parameters","--","--","--","--","--","model",":","``","autogl.module.model.BaseAutoModel","``","Currently","Heterogeneous","trainer","does","n't","support","decoupled","model","setting",".","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","."]
code: def _initialize(self):
      self.encoder.initialize()
  
imports: from .None import register_trainer
  from .base import BaseNodeClassificationHetTrainer, EarlyStopping
  import torch
  from torch.optim.lr_scheduler import StepLR, MultiStepLR, ExponentialLR, ReduceLROnPlateau
  import torch.nn.functional
  from ..model import BaseAutoModel
  from .evaluation import get_feval, Logloss
  from typing import Union
  from copy import deepcopy
  from sklearn.metrics import f1_score
  from ...utils import get_logger
  from ...backend import DependentBackend
label_desc: Use image node_id 2 for calling the NodeClassificationHetTrainer obj's underlying member method code with example usage: obj._initialize() without return types