code: def print_statistics(self, run=None):
      if run is not None:
          result = 100 * torch.tensor(self.results[run])
          argmax = result[:, 1].argmax().item()
          print(f"Run {run + 1:02d}:")
          print(f"Highest Train: {result[:, 0].max():.2f}")
          print(f"Highest Valid: {result[:, 1].max():.2f}")
          print(f"  Final Train: {result[argmax, 0]:.2f}")
          print(f"   Final Test: {result[argmax, 2]:.2f}")
      else:
          result = 100 * torch.tensor(self.results)
  
          best_results = []
          for r in result:
              train1 = r[:, 0].max().item()
              valid = r[:, 1].max().item()
              train2 = r[r[:, 1].argmax(), 0].item()
              test = r[r[:, 1].argmax(), 2].item()
              best_results.append((train1, valid, train2, test))
  
          best_result = torch.tensor(best_results)
  
          print(f"All runs:")
          r = best_result[:, 0]
          print(f"Highest Train: {r.mean():.2f} ± {r.std():.2f}")
          r = best_result[:, 1]
          print(f"Highest Valid: {r.mean():.2f} ± {r.std():.2f}")
          r = best_result[:, 2]
          print(f"  Final Train: {r.mean():.2f} ± {r.std():.2f}")
          r = best_result[:, 3]
          print(f"   Final Test: {r.mean():.2f} ± {r.std():.2f}")
  
file_path: dgl/examples/pytorch/ogb/ogbn-proteins/utils.py
imports: import random
  import numpy
  import torch
  import torch.nn.functional
  from models import MWE_DGCN, MWE_GCN
label_desc: Use image node_id 3 for calling the Logger obj's underlying member method code with example usage: obj.print_statistics(run) without return types