class_docstr: Implementation of control theory augment.
class_docstr_tok: ["Implementation","of","control","theory","augment","."]
docstr: Uniformly samples sequence of augmentation ops.
docstr_tok: ["Uniformly","samples","sequence","of","augmentation","ops","."]
code: def _sample_ops_uniformly(self):
      """Uniformly samples sequence of augmentation ops."""
      op_indices = tf.random.uniform(
          shape=[self.n], maxval=len(IMAGENET_AUG_OPS), dtype=tf.int32
      )
      op_args = tf.random.uniform(shape=[self.n], dtype=tf.float32)
      return op_indices, op_args
  
imports: from typing import Optional
  import tensorflow
  from .randaugment import augment_ops
label_desc: Use image node_id 5 for calling the CTAugment obj's underlying member method code with example usage: obj._sample_ops_uniformly() and returns: op_indices,  op_args