docstr: Create the Kinetics train partition from the list of video labels
  in {self.data_dir}/test
docstr_tok: ["Create","the","Kinetics","train","partition","from","the","list","of","video","labels","in","{","self.data_dir","}","\/test"]
code: def test_dataloader(
      self, test_batch_size: int = 1
  ) -> torch.utils.data.DataLoader:
      """
      Create the Kinetics train partition from the list of video labels
      in {self.data_dir}/test
      """
      transforms = self._video_transform(mode="test")
  
      test_dataset = pytorchvideo.data.Kinetics(
          data_path=os.path.join(self.data_dir, "test"),
          clip_sampler=pytorchvideo.data.make_clip_sampler(
              self.train_clip_sampler, self.clip_duration
          ),
          decode_audio=False,
          transform=transforms,
      )
      return torch.utils.data.DataLoader(
          test_dataset,
          batch_size=test_batch_size,
          num_workers=self.num_workers,
          pin_memory=self.pin_memory,
          collate_fn=self._custom_collate_val,
      )
  
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 7 for calling the KineticsDataModule obj's underlying member method code with example usage: obj.test_dataloader(test_batch_size) and returns: torch