docstr: batch: list of length batch_size
docstr_tok: ["batch",":","list","of","length","batch_size"]
code: def _custom_collate_val(self, batch) -> KineticsDataset:
      """
      batch: list of length batch_size
      """
      # transpose: input is [C, T, H, W] -> [T, C, H, W]
      for b in batch:
          b["video"] = b["video"].permute(1, 0, 2, 3).contiguous()
      batch = torch.utils.data.default_collate(batch)
      return KineticsDataset(video_tensor=batch["video"])
  
imports: import os
  from typing import List, Optional, Union
  import pytorchvideo.data
  import torch
  import torch.nn
  import torch.utils.data
  import torchvision
  from datamodules.video_data_api import KineticsDataset
  from pytorch_lightning import LightningDataModule
  from pytorchvideo.transforms import ApplyTransformToKey, Normalize, RemoveKey, UniformTemporalSubsample
  from torchvision.transforms import CenterCrop, Compose, Lambda, RandomChoice, RandomCrop, RandomHorizontalFlip, RandomResizedCrop
label_desc: Use image node_id 2 for calling the KineticsDataModule obj's underlying member method code with example usage: obj._custom_collate_val(batch) and returns: KineticsDataset