class_docstr: Horizontally flip the list of given images randomly
  with a probability 0.5
class_docstr_tok: ["Horizontally","flip","the","list","of","given","images","randomly","with","a","probability","0.5"]
docstr: Args:
  img (PIL.Image or numpy.ndarray): List of images to be cropped
  in format (h, w, c) in numpy.ndarray
  
  Returns:
  PIL.Image or numpy.ndarray: Randomly flipped clip
docstr_tok: ["Args",":","img","(","PIL.Image","or","numpy.ndarray",")",":","List","of","images","to","be","cropped","in","format","(","h",",","w",",","c",")","in","numpy.ndarray","Returns",":","PIL.Image","or","numpy.ndarray",":","Randomly","flipped","clip"]
code: def __call__(self, clip):
      """
      Args:
      img (PIL.Image or numpy.ndarray): List of images to be cropped
      in format (h, w, c) in numpy.ndarray
  
      Returns:
      PIL.Image or numpy.ndarray: Randomly flipped clip
      """
      if random.random() < 0.5:
          if isinstance(clip[0], np.ndarray):
              return [np.fliplr(img) for img in clip]
          elif isinstance(clip[0], PIL.Image.Image):
              return [
                  img.transpose(PIL.Image.FLIP_LEFT_RIGHT)
                  for img in clip
              ]
          else:
              raise TypeError(
                  "Expected numpy.ndarray or PIL.Image"
                  + " but got list of {0}".format(type(clip[0]))
              )
      return clip
  
imports: import numbers
  import random
  import numpy
  import PIL
  import torch
  import torchvision
  from .None import functional
label_desc: Use image node_id 1 for calling the RandomHorizontalFlip obj's underlying member method code with example usage: obj.__call__(clip) and returns: clip, unknown, unknown