class_docstr: Egocentric classification video clip-based dataset for
  `DoMSEV <https://www.verlab.dcc.ufmg.br/semantic-hyperlapse/cvpr2018-dataset/>`_
  stored as an encoded video (with frame-level labels).
  
  This dataset handles the loading, decoding, and configurable clip
  sampling for the videos.
class_docstr_tok: ["Egocentric","classification","video","clip-based","dataset","for","`","DoMSEV","<","https",":","\/\/www.verlab.dcc.ufmg.br\/semantic-hyperlapse\/cvpr2018-dataset\/",">","`","_","stored","as","an","encoded","video","(","with","frame-level","labels",")",".","This","dataset","handles","the","loading",",","decoding",",","and","configurable","clip","sampling","for","the","videos","."]
docstr: Transforms a given video clip, according to some pre-defined transforms
  and an optional user transform function (self._user_transform).
  
  Args:
      clip (Dict[str, Any]): The clip that will be transformed.
  
  Returns:
      (Dict[str, Any]) The transformed clip.
docstr_tok: ["Transforms","a","given","video","clip",",","according","to","some","pre-defined","transforms","and","an","optional","user","transform","function","(","self._user_transform",")",".","Args",":","clip","(","Dict","[","str",",","Any","]",")",":","The","clip","that","will","be","transformed",".","Returns",":","(","Dict","[","str",",","Any","]",")","The","transformed","clip","."]
code: def _transform_clip(self, clip: Dict[str, Any]) -> Dict[str, Any]:
      """
      Transforms a given video clip, according to some pre-defined transforms
      and an optional user transform function (self._user_transform).
  
      Args:
          clip (Dict[str, Any]): The clip that will be transformed.
  
      Returns:
          (Dict[str, Any]) The transformed clip.
      """
      for key in clip:
          if clip[key] is None:
              clip[key] = torch.tensor([])
  
      if self._user_transform:
          clip = self._user_transform(clip)
  
      return clip
  
imports: import logging
  import math
  import random
  import time
  from dataclasses import dataclass
  from enum import Enum
  from typing import Any, Callable, Dict, List, Optional, Tuple
  import numpy
  import torch
  from iopath.common.file_io import g_pathmgr
  from PIL import Image
  from pytorchvideo.data.dataset_manifest_utils import ImageDataset, ImageFrameInfo, VideoClipInfo, VideoDataset, VideoDatasetType
  from pytorchvideo.data.utils import DataclassFieldCaster, load_dataclass_dict_from_csv
  from pytorchvideo.data.video import Video
label_desc: Use image node_id 4 for calling the DomsevVideoDataset obj's underlying member method code with example usage: obj._transform_clip(clip) and returns: clip