code: def edge_init(edges):
      R_src, R_dst = edges.src["R"], edges.dst["R"]
      dist = torch.sqrt(F.relu(torch.sum((R_src - R_dst) ** 2, -1)))
      # d: bond length, o: bond orientation
      return {"d": dist, "o": R_src - R_dst}
  
imports: import copy
  from pathlib import Path
  import click
  import dgl
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from dgl.data.utils import Subset
  from logzero import logger
  from modules.dimenet import DimeNet
  from modules.dimenet_pp import DimeNetPP
  from modules.initializers import GlorotOrthogonal
  from qm9 import QM9
  from ruamel.yaml import YAML
  from sklearn.metrics import mean_absolute_error
  from torch.utils.data import DataLoader
label_desc: Use image node_id 3 for calling a global function with example usage: edge_init(edges) and returns: dict