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 from_dataset(self, dataset):
      self.register_parameter(
          "out_key", dataset.schema["target_node_type"]
      )
      self.register_parameter("edge_type", dataset[0].etypes)
  
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 3 for calling the AutoHeteroRGCN obj's underlying member method code with example usage: obj.from_dataset(dataset) without return types