class_docstr: Applies transform to key of dictionary input.
  
  Args:
      key (str): the dictionary key the transform is applied to
      transform (callable): the transform that is applied
  
  Example:
      >>>   transforms.ApplyTransformToKey(
      >>>       key='video',
      >>>       transform=UniformTemporalSubsample(num_video_samples),
      >>>   )
class_docstr_tok: ["Applies","transform","to","key","of","dictionary","input",".","Args",":","key","(","str",")",":","the","dictionary","key","the","transform","is","applied","to","transform","(","callable",")",":","the","transform","that","is","applied","Example",":",">",">",">","transforms.ApplyTransformToKey","(",">",">",">","key='video","'",",",">",">",">","transform=UniformTemporalSubsample","(","num_video_samples",")",",",">",">",">",")"]
code: def __init__(self, key: str, transform: Callable):
      self._key = key
      self._transform = transform
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import pytorchvideo.transforms.functional
  import torch
  import torchvision.transforms
label_desc: Use image node_id 1 to create a new ApplyTransformToKey object  with example: obj = ApplyTransformToKey(key, transform)