code: def compute_rank(ranks):
      # fair ranking prediction as the average
      # of optimistic and pessimistic ranking
      true = ranks[0]
      optimistic = (ranks > true).sum() + 1
      pessimistic = (ranks >= true).sum()
      return (optimistic + pessimistic).float() * 0.5
  
imports: import os.path
  import time
  import torch
  import torch.nn.functional
  from torch.nn import Parameter
  from tqdm import tqdm
  from torch_geometric.datasets import RelLinkPredDataset
  from torch_geometric.nn import GAE, RGCNConv
label_desc: Use image node_id 4 for calling a global function with example usage: compute_rank(ranks) and returns: unknown