hunk.code_window:     model = ResNet(**kwargs)
       model.default_cfg = copy.deepcopy(default_cfg)
       if kwargs.pop('pruned', False):
           model = adapt_model_from_file(model, variant)
       if pretrained:
           load_pretrained(
               model,
  
hunk.labels: keep keep replace keep keep keep keep
hunk.after_edit:     if pruned:
  
hunk.file_path: timm/models/resnet.py
hunk.type: replace
file: import math
  import torch
  from torch.utils.data import Sampler
  import torch.distributed as dist
  
  
  class OrderedDistributedSampler(Sampler):
      """Sampler that restricts data loading to a subset of the dataset.
      It is especially useful in conjunction with
      :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
      process can pass a DistributedSampler instance as a DataLoader sampler,
      and load a subset of the original dataset that is exclusive to it.
      .. note::
          Dataset is assumed to be of constant size.
      Arguments:
          dataset: Dataset used for sampling.
          num_replicas (optional): Number of processes participating in
              distributed training.
          rank (optional): Rank of the current process within num_replicas.
      """
  
      def __init__(self, dataset, num_replicas=None, rank=None):
          if num_replicas is None:
              if not dist.is_available():
                  raise RuntimeError("Requires distributed package to be available")
              num_replicas = dist.get_world_size()
          if rank is None:
              if not dist.is_available():
                  raise RuntimeError("Requires distributed package to be available")
              rank = dist.get_rank()
          self.dataset = dataset
          self.num_replicas = num_replicas
          self.rank = rank
          self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
          self.total_size = self.num_samples * self.num_replicas
  
      def __iter__(self):
          indices = list(range(len(self.dataset)))
  
          # add extra samples to make it evenly divisible
          indices += indices[:(self.total_size - len(indices))]
          assert len(indices) == self.total_size
  
          # subsample
          indices = indices[self.rank:self.total_size:self.num_replicas]
          assert len(indices) == self.num_samples
  
          return iter(indices)
  
      def __len__(self):
          return self.num_samples
  