class_docstr: Charades video loader. Construct the Charades video loader, then sample
  clips from the videos. For training and validation, a single clip is randomly
  sampled from every video with random cropping, scaling, and flipping. For
  testing, multiple clips are uniformaly sampled from every video with uniform
  cropping. For uniform cropping, we take the left, center, and right crop if
  the width is larger than height, or take top, center, and bottom crop if the
  height is larger than the width.
class_docstr_tok: ["Charades","video","loader",".","Construct","the","Charades","video","loader",",","then","sample","clips","from","the","videos",".","For","training","and","validation",",","a","single","clip","is","randomly","sampled","from","every","video","with","random","cropping",",","scaling",",","and","flipping",".","For","testing",",","multiple","clips","are","uniformaly","sampled","from","every","video","with","uniform","cropping",".","For","uniform","cropping",",","we","take","the","left",",","center",",","and","right","crop","if","the","width","is","larger","than","height",",","or","take","top",",","center",",","and","bottom","crop","if","the","height","is","larger","than","the","width","."]
docstr: Returns:
      (int): the number of videos in the dataset.
docstr_tok: ["Returns",":","(","int",")",":","the","number","of","videos","in","the","dataset","."]
code: def __len__(self):
      """
      Returns:
          (int): the number of videos in the dataset.
      """
      return self.num_videos
  
file_path: SlowFast/slowfast/datasets/charades.py
imports: import os
  import random
  from itertools import chain
  import torch
  import torch.utils.data
  import slowfast.utils.logging
  from slowfast.utils.env import pathmgr
  from .None import utils
  from .build import DATASET_REGISTRY
label_desc: Use image node_id 5 for calling the Charades obj's underlying member method code with example usage: obj.__len__() and returns: self