code: def testOneArmedAndNestedIf(self):
      def int_type(_):
          return instructions.TensorType(np.int32, ())
  
      ctx = frontend.Context()
  
      @ctx.batch(type_inference=int_type)
      def function(x):
          ans = 1
          if x > 4:
              if x > 10:
                  ans = 5
              else:
                  ans = 3
          return ans
  
      inputs = self._build_tensor(
          np.array([1, 5, 60, 3, 7], dtype=np.int32)
      )
      outputs = np.array([1, 3, 5, 1, 3], dtype=np.int32)
      # pylint: disable=unexpected-keyword-arg
      # The `max_stack_depth` and `backend` keyword arguments to `function`
      # are introduced by the `@ctx.batch` decorator, confusing pylint.
      answer = function(inputs, max_stack_depth=15, backend=TF_BACKEND)
      self._check_batch_size(answer, 5)
      self.assertAllEqual(outputs, self.evaluate(answer))
  
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 4 for calling the _TestHidingTFBatchSize obj's underlying member method code with example usage: obj.testOneArmedAndNestedIf() and returns: instructions, ans