class_docstr: Apply a list of transformations randomly given probability
  
  Parameters
  ----------
  transforms
      List of transformations.
  p : float
      Probability of applying the transformations.
  
  
  Inputs:
      - **data**: input tensor.
  
  Outputs:
      - **out**: transformed image.
class_docstr_tok: ["Apply","a","list","of","transformations","randomly","given","probability","Parameters","--","--","--","--","--","transforms","List","of","transformations",".","p",":","float","Probability","of","applying","the","transformations",".","Inputs",":","-","*","*","data","*","*",":","input","tensor",".","Outputs",":","-","*","*","out","*","*",":","transformed","image","."]
code: def __init__(self, transforms, p=0.5):
      super(RandomApply, self).__init__()
      self.transforms = transforms
      self.p = p
  
imports: import warnings
  import random
  from ....block import Block, HybridBlock
  from ....nn import Sequential, HybridSequential
  from .....util import use_np
  from .....None import np, npx
  from .image import *
  from .image import _append_return
label_desc: Use image node_id 1 to create a new RandomApply object from inherited base classes: Sequential with example: obj = RandomApply(transforms, p)