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: Given the video index, return the list of sampled frame indexes.
  Args:
      index (int): the video index.
  Returns:
      seq (list): the indexes of frames of sampled from the video.
docstr_tok: ["Given","the","video","index",",","return","the","list","of","sampled","frame","indexes",".","Args",":","index","(","int",")",":","the","video","index",".","Returns",":","seq","(","list",")",":","the","indexes","of","frames","of","sampled","from","the","video","."]
code: def get_seq_frames(self, index):
      """
      Given the video index, return the list of sampled frame indexes.
      Args:
          index (int): the video index.
      Returns:
          seq (list): the indexes of frames of sampled from the video.
      """
      num_frames = self.cfg.DATA.NUM_FRAMES
      video_length = len(self._path_to_videos[index])
  
      seg_size = float(video_length - 1) / num_frames
      seq = []
      for i in range(num_frames):
          start = int(np.round(seg_size * i))
          end = int(np.round(seg_size * (i + 1)))
          if self.mode == "train":
              seq.append(random.randint(start, end))
          else:
              seq.append((start + end) // 2)
  
      return seq
  
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 3 for calling the Ssv2 obj's underlying member method code with example usage: obj.get_seq_frames(index) and returns: seq