class_docstr: Implementation of control theory augment.
class_docstr_tok: ["Implementation","of","control","theory","augment","."]
code: def __call__(
      self, image, probe: bool = True, aug_image_key: str = "image"
  ):
      # creating local variable which will store copy of CTA log probabilities
      with tf.variable_creator_scope(_skip_mirrored_creator):
          local_log_prob = tf.Variable(
              lambda: tf.ones(self.state_shape, dtype=tf.float32),
              trainable=False,
              name="cta_log_probs",
          )
      self.log_probs.append(local_log_prob)
  
      output_dict = {}
      if probe:
          probe_op_indices, probe_op_args = self._sample_ops_uniformly()
          probe_image = self._apply_ops(
              image, probe_op_indices, probe_op_args
          )
          output_dict["probe_op_indices"] = probe_op_indices
          output_dict["probe_op_args"] = probe_op_args
          output_dict["probe_image"] = probe_image
  
      if aug_image_key is not None:
          op_indices, op_args = self._sample_ops(local_log_prob)
          aug_image = self._apply_ops(
              image, op_indices, op_args, prob_to_apply=self.p
          )
          output_dict[aug_image_key] = aug_image
  
      if aug_image_key != "image":
          output_dict["image"] = image
  
      return output_dict
  
imports: from typing import Optional
  import tensorflow
  from .randaugment import augment_ops
label_desc: Use image node_id 8 for calling the CTAugment obj's underlying member method code with example usage: obj.__call__(image, probe, aug_image_key) and returns: output_dict