code: def testDryRunIf(self):
      def id_type(args):
          return args[0]
  
      truthy = frontend.truthy
  
      def batch_abs(x):
          if truthy(x > 0):
              return x
          else:
              return -x
  
      batched = frontend.Context().batch_uncurried(batch_abs, id_type)
      inputs = np.array([12, -13, 14], dtype=np.int64)
      self.assertAllEqual(
          [12, 13, 14],
          self.evaluate(
              batched(inputs, max_stack_depth=15, backend=TF_BACKEND)
          ),
      )
  
      # Note: trying to dry-run control flow will only work in Eager mode, because
      # Graph-mode Tensors cannot be used as `if` conditions at all.
      if tf.executing_eagerly():
          self.assertEqual(
              [12],
              self.evaluate(batched(tf.constant([12]), dry_run=True)),
          )
          self.assertEqual(
              [13],
              self.evaluate(batched(tf.constant([-13]), dry_run=True)),
          )
  
imports: import collections
  from absl import logging
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.auto_batching import frontend
  from tensorflow_probability.python.experimental.auto_batching import instructions
  from tensorflow_probability.python.experimental.auto_batching import numpy_backend
  from tensorflow_probability.python.experimental.auto_batching import tf_backend
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 10 for calling the AutoGraphFrontendTest obj's underlying member method code with example usage: obj.testDryRunIf() and returns: args, x, unknown