class_docstr: This class was taken directly from the PyG implementation and can be found
  here: https://github.com/snap-stanford/ogb/blob/master/examples/nodeproppred/mag/logger.py
  
  This was done to ensure that performance was measured in precisely the same way
class_docstr_tok: ["This","class","was","taken","directly","from","the","PyG","implementation","and","can","be","found","here",":","https",":","\/\/github.com\/snap-stanford\/ogb\/blob\/master\/examples\/nodeproppred\/mag\/logger.py","This","was","done","to","ensure","that","performance","was","measured","in","precisely","the","same","way"]
code: def print_statistics(self, run=None):
      if run is not None:
          result = 100 * th.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 * th.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 = th.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}")
  
imports: import argparse
  import itertools
  import sys
  import dgl
  import dgl.nn
  import psutil
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl import AddReverse, Compose, ToSimple
  from dgl.nn import HeteroEmbedding
  from ogb.nodeproppred import DglNodePropPredDataset, Evaluator
  from tqdm import tqdm
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