class_docstr: An utility transform that repeats each value in a
  key, value-style minibatch and replaces it with a list of values.
  
  Useful for performing multiple augmentations.
  An example such usecase can be found in
  `pytorchvideo_trainer/conf/datamodule/transforms/kinetics_classification_mvit_16x4.yaml`
  
  Args:
      repead_num (int): Number of times to repeat each value.
class_docstr_tok: ["An","utility","transform","that","repeats","each","value","in","a","key",",","value-style","minibatch","and","replaces","it","with","a","list","of","values",".","Useful","for","performing","multiple","augmentations",".","An","example","such","usecase","can","be","found","in","`","pytorchvideo_trainer\/conf\/datamodule\/transforms\/kinetics_classification_mvit_16x4.yaml","`","Args",":","repead_num","(","int",")",":","Number","of","times","to","repeat","each","value","."]
code: def __call__(
      self, sample_dict: Dict[str, Any]
  ) -> Dict[str, List[Any]]:
      for k, v in sample_dict.items():
          sample_dict[k] = self.repeat_num * [v]
      return sample_dict
  
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 2 for calling the RepeatandConverttoList obj's underlying member method code with example usage: obj.__call__(sample_dict) and returns: sample_dict