class_docstr: Applies transform to key of dictionary input wherein input is a list
  
  Args:
      key (str): the dictionary key the transform is applied to
      transform (callable): the transform that is applied
  
  Example:
       >>>  transforms.ApplyTransformToKeyOnList(
      >>>       key='video',
      >>>       transform=UniformTemporalSubsample(num_video_samples),
      >>>   )
class_docstr_tok: ["Applies","transform","to","key","of","dictionary","input","wherein","input","is","a","list","Args",":","key","(","str",")",":","the","dictionary","key","the","transform","is","applied","to","transform","(","callable",")",":","the","transform","that","is","applied","Example",":",">",">",">","transforms.ApplyTransformToKeyOnList","(",">",">",">","key='video","'",",",">",">",">","transform=UniformTemporalSubsample","(","num_video_samples",")",",",">",">",">",")"]
code: def __init__(
      self, key: str, transform: Callable
  ) -> None:  # pyre-ignore[24]
      self._key = key
      self._transform = transform
  
imports: import random
  from typing import Any, Callable, Dict, Iterable, List, Mapping, Sequence
  import hydra
  import torch
  import torchvision
  from PIL import Image, ImageFilter
  from pytorchvideo.transforms import MixVideo
  from torchvision.transforms import Compose
label_desc: Use image node_id 1 to create a new ApplyTransformToKeyOnList object  with example: obj = ApplyTransformToKeyOnList(key, transform)