code: def forward(self, g, h):
      with g.local_scope():
          g.ndata["h"] = h
          # Compute a new edge feature named 'score' by a dot-product between the
          # source node feature 'h' and destination node feature 'h'.
          g.apply_edges(fn.u_dot_v("h", "h", "score"))
          # u_dot_v returns a 1-element vector for each edge so you need to squeeze it.
          return g.edata["score"][:, 0]
  
imports: import itertools
  import os
  import dgl
  import dgl.data
  import numpy
  import scipy.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn import SAGEConv
  import dgl.function
  from sklearn.metrics import roc_auc_score
label_desc: Use image node_id 1 for calling the DotPredictor obj's underlying member method code with example usage: obj.forward(g, h) and returns: g