code: def __getitem__(self, idx):
      if self.split == "train":
          # assume we only sample 1 clip from each training video
          target_seed_offset = idx
      else:
          # for video model testing, clips from the same video share the same
          # target label
          target_seed_offset = idx // self.clips_per_video
      with torch_seed(self.seed + target_seed_offset):
          target = torch.randint(0, self.num_classes, (1,)).item()
  
      with torch_seed(self.seed + idx):
          return {
              "input": {
                  "video": torch.randint(
                      0,
                      256,
                      (
                          self.frames_per_clip,
                          self.video_height,
                          self.video_width,
                          self.video_channels,
                      ),
                      dtype=torch.uint8,
                  ),
                  "audio": torch.rand(
                      (self.audio_samples, 1), dtype=torch.float
                  ),
              },
              "target": target,
          }
  
imports: import torch
  from ...generic.util import torch_seed
label_desc: Use image node_id 2 for calling the RandomVideoDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: dict