class_docstr: Implementation of control theory augment.
class_docstr_tok: ["Implementation","of","control","theory","augment","."]
docstr: Samples sequence of augmentation ops using current probabilities.
docstr_tok: ["Samples","sequence","of","augmentation","ops","using","current","probabilities","."]
code: def _sample_ops(self, local_log_prob):
      """Samples sequence of augmentation ops using current probabilities."""
      # choose operations
      op_indices = tf.random.uniform(
          shape=[self.n], maxval=len(IMAGENET_AUG_OPS), dtype=tf.int32
      )
      # sample arguments for each selected operation
      selected_ops_log_probs = tf.gather(
          local_log_prob, op_indices, axis=0
      )
      op_args = tf.random.categorical(
          selected_ops_log_probs, num_samples=1
      )
      op_args = tf.cast(tf.squeeze(op_args, axis=1), tf.float32)
      op_args = (op_args + tf.random.uniform([self.n])) / self.quantum
      return op_indices, op_args
  
imports: from typing import Optional
  import tensorflow
  from .randaugment import augment_ops
label_desc: Use image node_id 6 for calling the CTAugment obj's underlying member method code with example usage: obj._sample_ops(local_log_prob) and returns: op_indices,  op_args