class_docstr: Loads images or videos from the Vimeo-90k septuplet dataset [1]. The dataset
  consists of a set of septuplet directories, where each directory contains
  seven consecutive frames of a video from vimeo.com. Each frame is stored as
  a PNG file. This class can be configured to return videos (consecutive
  frames in a septuplet) or individual images every time an item is accessed.
  
  Xue, Tianfan, et al. "Video enhancement with task-oriented flow."
  International Journal of Computer Vision 127.8 (2019): 1106-1125.
  
  Note:
      Following the conventions of ``torchvision``, in video mode this
      dataset will have a default transform of
      ``torchvision.transforms.ToTensor``, while in image mode no default
      transform is provided.
  
  Args:
      root: Path to the Vimeo-90k root directory (i.e. the
          directory containing the dataset's README).
      as_video: Determines whether the dataset should return
          individual images (``as_video=False``) or
          multiple consecutive frames  (``as_video=True``)
          at a time.
      frames_per_group: The number of frames to include from
          each septuplet. Specifically, the first ``frames_per_group``
          frames from each septuplet are included in
          the dataset. Must be between 1 and 7.
      split: Specifies which dataset parition should be used. Valid values
          are ``"train"`` or ``"test"``. Exactly one of ``split`` or
          'folder_list' must be specified.
      folder_list: A list of paths to septuplets to include in the dataset
          split. Each septuplet path must be a directory containing the files
          ``im1.png``, ..., ``im7.png``. Exactly one of ``split`` or
          ``folder_list`` must be specified.
      pil_transform: Callable object for applying transforms to
          the PIL images prior to image concatenation. If using, be sure to
          have the final operation convert the PIL image to a tensor.
          Following ``torchvision``'s dataset conventions, the default
          transform is ``torchvision.transforms.ToTensor`` when
          in video mode (i.e. when ``as_video=True``), while no
          default transform is applied in image mode.
      tensor_transform: Callable object for applying PyTorch
          transforms after data conversion and septuplet concatenation.
code: def load_image(self, septuplet: Path, frame_number: int) -> Tensor:
      img_path = default_loader(
          str(septuplet / f"im{frame_number}.png")
      )
      img = self.pil_transform(img_path)
      if not isinstance(img, Tensor):
          raise RuntimeError(
              "Tensor not returned from pil_transform. "
              "Did you forget to add ToTensor() to your transform?"
          )
      return img
  
file_path: NeuralCompression/neuralcompression/data/_vimeo_90k_septuplet.py
imports: import os
  from pathlib import Path
  from typing import Any, Callable, Optional, Sequence, Union
  import torch
  from torch import Tensor
  from torch.utils.data import Dataset
  from torchvision.datasets.folder import default_loader
  from torchvision.transforms import ToTensor
label_desc: Use image node_id 3 for calling the Vimeo90kSeptuplet obj's underlying member method code with example usage: obj.load_image(septuplet, frame_number) and returns: img