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.
class_docstr_tok: ["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","."]
docstr: internal feature layer
docstr_tok: ["internal","feature","layer"]
code: def __init_inter_feat_layer__(self):
      """internal feature layer"""
  
      self.embedding = torch.nn.Parameter(
          torch.Tensor(
              self.in_channels, self.setting.inter_feat_channels
          )
      )
      self.embedding.requires_grad = True
  
      # reset parameters to be normally distributed
      self.embedding.data.normal_()
  
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 2 for calling the GripNetInternalModule obj's underlying member method code with example usage: obj.__init_inter_feat_layer__() without return types