class_docstr: Implementation of control theory augment.
class_docstr_tok: ["Implementation","of","control","theory","augment","."]
code: def _apply_ops(self, image, op_indices, op_args, prob_to_apply=None):
      for idx in range(self.n):
          image = apply_augmentation_op(
              image, op_indices[idx], op_args[idx], prob_to_apply
          )
      return image
  
imports: from typing import Optional
  import tensorflow
  from .randaugment import augment_ops
label_desc: Use image node_id 7 for calling the CTAugment obj's underlying member method code with example usage: obj._apply_ops(image, op_indices, op_args, prob_to_apply) and returns: image