class_docstr: The internal module of a supervertex, which is composed of an internal feature layer and multiple internal
  aggregation layers.
  
  Args:
      in_channels (int): the dimension of node features on this supervertex.
      num_edge_type (int): the number of edge types on this supervertex.
      start_supervertex (bool): whether this supervertex is a start supervertex on the supergraph.
      setting (SuperVertexParaSetting): supervertex parameter settings.
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code: def __init__(
      self,
      in_channels: int,
      num_edge_type: int,
      start_supervertex: bool,
      setting: SuperVertexParaSetting,
  ) -> None:
      super(GripNetInternalModule, self).__init__()
      # in and out dimension
      self.in_channels = in_channels
      self.out_channels = setting.inter_agg_channels_list[-1]
  
      self.num_edge_type = num_edge_type
      self.multirelational = 1 if num_edge_type > 1 else 0
      self.start_supervertex = start_supervertex
      self.setting = setting
  
      self.__init_inter_feat_layer__()
      self.__init_inter_agg_layer__()
  
imports: import logging
  from typing import Dict
  import torch
  import torch.nn.functional
  from torch.nn import Module
  from kale.embed.gcn import GCNEncoderLayer, RGCNEncoderLayer
  from kale.prepdata.supergraph_construct import SuperGraph, SuperVertex, SuperVertexParaSetting
label_desc: Use image node_id 1 to create a new GripNetInternalModule object from inherited base classes: Module with example: obj = GripNetInternalModule(in_channels, num_edge_type, start_supervertex, setting)