class_docstr: AutoHGT.
  The model used in this automodel is HGT, i.e., the graph convolutional network from the
  `"Heterogeneous Graph Transformer" <https://arxiv.org/abs/2003.01332>`_ paper.
      
  Parameters
  ----------
  num_features: `int`.
      The dimension of features.
  
  num_classes: `int`.
      The number of classes.
  
  device: `torch.device` or `str`.
      The device where model will be running on.
  
  init: `bool`.
      If True(False), the model will (not) be initialized.
  
  dataset: `autogl.datasets`.
      Hetero Graph Dataset in autogl.
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code: def _initialize(self):
      self._model = HGT(
          dict(
              features_num=self.input_dimension,
              num_class=self.output_dimension,
              out_key=self.out_key,
              node_dict=self.node_dict,
              edge_dict=self.edge_dict,
              **self.hyper_parameters,
          )
      ).to(self.device)
  
imports: import logging
  import dgl
  import math
  import torch
  import torch.nn
  import torch.nn.functional
  import dgl.function
  from dgl.nn.functional import edge_softmax
  from ..None import register_model
  from .base import BaseHeteroModelMaintainer
  from ..base import activate_func
  from .....utils import get_logger
label_desc: Use image node_id 2 for calling the AutoHGT obj's underlying member method code with example usage: obj._initialize() without return types