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 normalize_input(self, node_feature):
      return (node_feature - self.stat["median"]) * (
          2 / (self.stat["max"] - self.stat["min"])
      )
  
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 2 for calling the PrepareLayer obj's underlying member method code with example usage: obj.normalize_input(node_feature) and returns: unknown