class_docstr: AutoHeteroRGCN.
  The model used in this automodel is HeteroRGCN, i.e., the relational graph convolutional network from the
  `"Modeling Relational Data with Graph Convolutional Networks"
  <https://arxiv.org/abs/1703.06103>`_ 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 __init__(
      self,
      num_features=None,
      num_classes=None,
      device=None,
      init=False,
      dataset=None,
      **args,
  ):
      super().__init__(num_features, num_classes, device, dataset)
  
      self.hyper_parameter_space = [
          {
              "parameterName": "num_layers",
              "type": "DISCRETE",
              "feasiblePoints": "2,3,4",
          },
          {
              "parameterName": "hidden",
              "type": "NUMERICAL_LIST",
              "numericalType": "INTEGER",
              "length": 3,
              "minValue": [8, 8, 8],
              "maxValue": [64, 64, 64],
              "scalingType": "LOG",
              "cutPara": ("num_layers",),
              "cutFunc": lambda x: x[0] - 1,
          },
          {
              "parameterName": "dropout",
              "type": "DOUBLE",
              "maxValue": 0.8,
              "minValue": 0.2,
              "scalingType": "LINEAR",
          },
          {
              "parameterName": "act",
              "type": "CATEGORICAL",
              "feasiblePoints": ["leaky_relu", "relu", "elu", "tanh"],
          },
      ]
  
      self.hyper_parameters = {
          "num_layers": 2,
          "hidden": [256],
          "act": "leaky_rely",
      }
  
      if init is True:
          self.initialize()
  
file_path: AutoGL/autogl/module/model/dgl/hetero/HeteroRGCN.py
imports: import torch.nn
  import dgl.function
  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 1 to create a new AutoHeteroRGCN object from inherited base classes: BaseHeteroModelMaintainer with example: obj = AutoHeteroRGCN(num_features, num_classes, device, init, dataset)