class_docstr: Randomly flip the images left to right with a probability.
  
  Parameters
  ----------
  prob : float
      The probability value to flip the images.
  
  Inputs:
      - **data**: a list of frames with shape [H x W x C]
  
  Outputs:
      - **out**: a list of flipped frames with shape [H x W x C]
class_docstr_tok: ["Randomly","flip","the","images","left","to","right","with","a","probability",".","Parameters","--","--","--","--","--","prob",":","float","The","probability","value","to","flip","the","images",".","Inputs",":","-","*","*","data","*","*",":","a","list","of","frames","with","shape","[","H","x","W","x","C","]","Outputs",":","-","*","*","out","*","*",":","a","list","of","flipped","frames","with","shape","[","H","x","W","x","C","]"]
code: def forward(self, clips):
      new_clips = []
      if random.random() < self.prob:
          for cur_img in clips:
              new_clips.append(np.flip(cur_img, axis=1))
      else:
          new_clips = clips
      return new_clips
  
imports: from __future__ import division
  import random
  import math
  import numbers
  import numpy
  from mxnet.gluon import Block
label_desc: Use image node_id 2 for calling the VideoRandomHorizontalFlip obj's underlying member method code with example usage: obj.forward(clips) and returns: new_clips