hunk.code_window:     """
       def __init__(
               self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='avg',
               embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=None,
               drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='skip', class_token=False,
               rel_pos_type='mlp', shared_rel_pos=False, fc_norm=False,
               embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=RelPosBlock):
           """
           Args:
               img_size (int, tuple): input image size
  
hunk.labels: keep keep keep keep replace replace replace keep keep keep keep
hunk.after_edit:             embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=1e-5,
               class_token=False, rel_pos_type='mlp', shared_rel_pos=False, fc_norm=False,
               drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='skip',
  
hunk.file_path: timm/models/vision_transformer_relpos.py
hunk.type: replace
file: """ Real labels evaluator for ImageNet
  Paper: `Are we done with ImageNet?` - https://arxiv.org/abs/2006.07159
  Based on Numpy example at https://github.com/google-research/reassessed-imagenet
  
  Hacked together by / Copyright 2020 Ross Wightman
  """
  import os
  import json
  import numpy as np
  
  
  class RealLabelsImagenet:
  
      def __init__(self, filenames, real_json='real.json', topk=(1, 5)):
          with open(real_json) as real_labels:
              real_labels = json.load(real_labels)
              real_labels = {f'ILSVRC2012_val_{i + 1:08d}.JPEG': labels for i, labels in enumerate(real_labels)}
          self.real_labels = real_labels
          self.filenames = filenames
          assert len(self.filenames) == len(self.real_labels)
          self.topk = topk
          self.is_correct = {k: [] for k in topk}
          self.sample_idx = 0
  
      def add_result(self, output):
          maxk = max(self.topk)
          _, pred_batch = output.topk(maxk, 1, True, True)
          pred_batch = pred_batch.cpu().numpy()
          for pred in pred_batch:
              filename = self.filenames[self.sample_idx]
              filename = os.path.basename(filename)
              if self.real_labels[filename]:
                  for k in self.topk:
                      self.is_correct[k].append(
                          any([p in self.real_labels[filename] for p in pred[:k]]))
              self.sample_idx += 1
  
      def get_accuracy(self, k=None):
          if k is None:
              return {k: float(np.mean(self.is_correct[k])) * 100 for k in self.topk}
          else:
              return float(np.mean(self.is_correct[k])) * 100
  