class_docstr: Simple Interaction Network
  One Layer interaction network for stellar multi-body problem simulation,
  it has the ability to simulate number of body motion no more than 12
  Parameters
  ==========
  node_feats : int
      Number of node features
  
  stat : dict
      Statistcics for Denormalization
class_docstr_tok: ["Simple","Interaction","Network","One","Layer","interaction","network","for","stellar","multi-body","problem","simulation",",","it","has","the","ability","to","simulate","number","of","body","motion","no","more","than","12","Parameters","==========","node_feats",":","int","Number","of","node","features","stat",":","dict","Statistcics","for","Denormalization"]
code: def forward(self, g, n_feat, e_feat, global_feats, relation_feats):
      with g.local_scope():
          out_n, out_e = self.in_layer(
              g, n_feat, e_feat, global_feats, relation_feats
          )
          out_n = self.denormalize_output(out_n)
          return out_n, out_e
  
imports: import copy
  from functools import partial
  import dgl
  import dgl.function
  import dgl.nn
  import torch
  import torch.nn
  from torch.nn import functional
label_desc: Use image node_id 3 for calling the InteractionNet obj's underlying member method code with example usage: obj.forward(g, n_feat, e_feat, global_feats, relation_feats) and returns: out_n,  out_e