class_docstr: Randomly selects between transforms1 and transforms2 ~ [p, 1 - p] .
code: def __call__(self, features):
      rnd = tf.random.uniform(
          [], minval=0.0, maxval=1.0, dtype=tf.float32
      )
      if rnd < self.p:
          return self.transforms1(identity(features))
      else:
          return self.transforms2(identity(features))
  
imports: from typing import Any, Dict
  import tensorflow
label_desc: Use image node_id 2 for calling the RandomSelect obj's underlying member method code with example usage: obj.__call__(features) and returns: self, self