class_docstr: Generate edge feature for the model input preparation:
  as well as do the normalization work.
  Parameters
  ==========
  node_feats : int
      Number of node features
  
  stat : dict
      dictionary which represent the statistics needed for normalization
class_docstr_tok: ["Generate","edge","feature","for","the","model","input","preparation",":","as","well","as","do","the","normalization","work",".","Parameters","==========","node_feats",":","int","Number","of","node","features","stat",":","dict","dictionary","which","represent","the","statistics","needed","for","normalization"]
code: def __init__(self, node_feats, stat):
      super(PrepareLayer, self).__init__()
      self.node_feats = node_feats
      # stat {'median':median,'max':max,'min':min}
      self.stat = stat
  
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 1 to create a new PrepareLayer object from inherited base classes: nn with example: obj = PrepareLayer(node_feats, stat)