code: def accuracy_from_loader(algorithm, loader, weights, debug=False):
      correct = 0
      total = 0
      losssum = 0.0
      weights_offset = 0
  
      algorithm.eval()
  
      for i, batch in enumerate(loader):
          x = batch["x"].to(device)
          y = batch["y"].to(device)
  
          with torch.no_grad():
              logits = algorithm.predict(x)
              loss = F.cross_entropy(logits, y).item()
  
          B = len(x)
          losssum += loss * B
  
          if weights is None:
              batch_weights = torch.ones(len(x))
          else:
              batch_weights = weights[
                  weights_offset : weights_offset + len(x)
              ]
              weights_offset += len(x)
          batch_weights = batch_weights.to(device)
          if logits.size(1) == 1:
              correct += (
                  (logits.gt(0).eq(y).float() * batch_weights)
                  .sum()
                  .item()
              )
          else:
              correct += (
                  (logits.argmax(1).eq(y).float() * batch_weights)
                  .sum()
                  .item()
              )
          total += batch_weights.sum().item()
  
          if debug:
              break
  
      algorithm.train()
  
      acc = correct / total
      loss = losssum / total
      return acc, loss
  
file_path: generalized-moe/domainbed/evaluator.py
imports: import collections
  import torch
  import torch.nn
  import torch.nn.functional
  from domainbed.lib.fast_data_loader import FastDataLoader
label_desc: Use image node_id 1 for calling a global function with example usage: accuracy_from_loader(algorithm, loader, weights, debug) and returns: acc,  loss