class_docstr: A sampler wrapper for grouping images with similar aspect ratio (< 1 or.
  
  >= 1) into a same batch.
  
  Args:
      sampler (Sampler): Base sampler.
      batch_size (int): Size of mini-batch.
      drop_last (bool): If ``True``, the sampler will drop the last batch if
          its size would be less than ``batch_size``.
class_docstr_tok: ["A","sampler","wrapper","for","grouping","images","with","similar","aspect","ratio","(","<","1","or",".",">","=","1",")","into","a","same","batch",".","Args",":","sampler","(","Sampler",")",":","Base","sampler",".","batch_size","(","int",")",":","Size","of","mini-batch",".","drop_last","(","bool",")",":","If","``","True","``",",","the","sampler","will","drop","the","last","batch","if","its","size","would","be","less","than","``","batch_size","``","."]
code: def __len__(self) -> int:
      if self.drop_last:
          return len(self.sampler) // self.batch_size
      else:
          return (
              len(self.sampler) + self.batch_size - 1
          ) // self.batch_size
  
file_path: mmdetection/mmdet/datasets/samplers/batch_sampler.py
imports: from typing import Sequence
  from torch.utils.data import BatchSampler, Sampler
  from mmdet.datasets.samplers.track_img_sampler import TrackImgSampler
  from mmdet.registry import DATA_SAMPLERS
label_desc: Use image node_id 3 for calling the AspectRatioBatchSampler obj's underlying member method code with example usage: obj.__len__() and returns: unknown, unknown