class_docstr: PyTorch Lightning data module version of ``Vimeo90kSeptuplet``.
  
  Note:
      The ``Vimeo90kSeptuplet`` dataloader by default will return images in
      the range ``[0, 1)`` without needing to add a normalization transform.
  
  Args:
      data_dir: root directory of Vimeo dataset.
      frames_per_group: Number of frames to load for the batch.
      train_batch_size: The batch size to use during training.
      val_batch_size: The batch size to use during validation.
      image_size: The size of the crop to take from the original images.
      num_workers: The number of parallel workers to create to load data
          items (see PyTorch's Dataloader documentation for more details).
      pin_memory: Whether prepared items should be loaded into pinned memory
          or not. This improves performance on GPUs.
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code: def val_dataloader(self) -> DataLoader:
      return DataLoader(
          self.val_dataset,
          batch_size=self.val_batch_size,
          num_workers=self.num_workers,
          shuffle=False,
          pin_memory=self.pin_memory,
      )
  
file_path: NeuralCompression/projects/deep_video_compression/data_module.py
imports: from typing import Optional, Sequence, Union
  from pytorch_lightning import LightningDataModule
  from torch.utils.data import DataLoader
  from torchvision import transforms
  from neuralcompression.data import Vimeo90kSeptuplet
label_desc: Use image node_id 4 for calling the Vimeo90kSeptupletLightning obj's underlying member method code with example usage: obj.val_dataloader() and returns: DataLoader