code: def _compute_aspect_ratios_slow(dataset, indices=None):
      print(
          "Your dataset doesn't support the fast path for "
          "computing the aspect ratios, so will iterate over "
          "the full dataset and load every image instead. "
          "This might take some time..."
      )
      if indices is None:
          indices = range(len(dataset))
  
      class SubsetSampler(Sampler):
          def __init__(self, indices):
              self.indices = indices
  
          def __iter__(self):
              return iter(self.indices)
  
          def __len__(self):
              return len(self.indices)
  
      sampler = SubsetSampler(indices)
      data_loader = torch.utils.data.DataLoader(
          dataset,
          batch_size=1,
          sampler=sampler,
          num_workers=14,  # you might want to increase it for faster processing
          collate_fn=lambda x: x[0],
      )
      aspect_ratios = []
      with tqdm(total=len(dataset)) as pbar:
          for _i, (img, _) in enumerate(data_loader):
              pbar.update(1)
              height, width = img.shape[-2:]
              aspect_ratio = float(width) / float(height)
              aspect_ratios.append(aspect_ratio)
      return aspect_ratios
  
imports: import bisect
  from collections import defaultdict
  import copy
  from itertools import repeat, chain
  import math
  import numpy
  import torch
  import torch.utils.data
  from torch.utils.data.sampler import BatchSampler, Sampler
  from torch.utils.model_zoo import tqdm
  import torchvision
  from PIL import Image
label_desc: Use image node_id 2 for calling a global function with example usage: _compute_aspect_ratios_slow(dataset, indices) and returns: aspect_ratios, iter, len