class_docstr: Transform for converting video frames as a list of tensors. Each tensor
  corresponding to a unique pathway.
class_docstr_tok: ["Transform","for","converting","video","frames","as","a","list","of","tensors",".","Each","tensor","corresponding","to","a","unique","pathway","."]
code: def forward(self, x: torch.Tensor):
      return utils.pack_pathway_output(self.cfg, x)
  
imports: import functools
  import os
  from typing import Dict
  import torch
  from torch.utils.data import DistributedSampler, RandomSampler, SequentialSampler
  from torchvision.transforms import Compose, Lambda
  from torchvision.transforms._transforms_video import NormalizeVideo, RandomCropVideo, RandomHorizontalFlipVideo
  import slowfast.utils.logging
  from pytorchvideo.data import Charades, LabeledVideoDataset, SSv2, make_clip_sampler
  from pytorchvideo.data.labeled_video_paths import LabeledVideoPaths
  from pytorchvideo.transforms import ApplyTransformToKey, RandomShortSideScale, ShortSideScale, UniformCropVideo, UniformTemporalSubsample
  from .None import utils
  from .build import DATASET_REGISTRY
label_desc: Use image node_id 2 for calling the PackPathway obj's underlying member method code with example usage: obj.forward(x) and returns: utils