class_docstr: Something-Something v2 (SSV2) video loader. Construct the SSV2 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: ["Something-Something","v2","(","SSV2",")","video","loader",".","Construct","the","SSV2","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/ssv2.py
imports: import json
  import numpy
  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 Ssv2 obj's underlying member method code with example usage: obj.__len__() and returns: self