class_docstr: Apply a list of transformations randomly given probability
  
  Parameters
  ----------
  transforms
      List of transformations which must be HybridBlocks.
  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","which","must","be","HybridBlocks",".","p",":","float","Probability","of","applying","the","transformations",".","Inputs",":","-","*","*","data","*","*",":","input","tensor",".","Outputs",":","-","*","*","out","*","*",":","transformed","image","."]
code: def forward(self, x, *args):
      cond = lambda p: p < np.random.uniform(low=0, high=1, size=1)
      return npx.cond(cond, x, self.transforms(x), self.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 2 for calling the HybridRandomApply obj's underlying member method code with example usage: obj.forward(x) and returns: npx