docstr: Create the Kinetics train partition from the list of video labels
  in {self.data_dir}/train. Add transform that subsamples and normalizes the
  video before applying the scale, crop and flip augmentations
docstr_tok: ["Create","the","Kinetics","train","partition","from","the","list","of","video","labels","in","{","self.data_dir","}","\/train",".","Add","transform","that","subsamples","and","normalizes","the","video","before","applying","the","scale",",","crop","and","flip","augmentations"]
code: def train_dataloader(self) -> torch.utils.data.DataLoader:
      """
      Create the Kinetics train partition from the list of video labels
      in {self.data_dir}/train. Add transform that subsamples and normalizes the
      video before applying the scale, crop and flip augmentations
      """
      return torch.utils.data.DataLoader(
          self.train_dataset,
          batch_size=self.train_batch_size,
          num_workers=self.num_workers,
          pin_memory=self.pin_memory,
          collate_fn=self._custom_collate_train,
      )
  
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 5 for calling the KineticsDataModule obj's underlying member method code with example usage: obj.train_dataloader() and returns: torch