code: def testEvenOddWithContext(self):
      def pred_type(_):
          return instructions.TensorType(np.int32, ())
  
      ab = frontend.Context()
  
      @ab.batch(type_inference=pred_type)
      def even(n):
          if n <= 0:
              return True
          else:
              return odd(n - 1)
  
      @ab.batch(type_inference=pred_type)
      def odd(n):
          if n <= 0:
              return False
          else:
              return even(n - 1)
  
      inputs = np.array([5, 6, 8, 9], dtype=np.int32)
      outputs = np.array([False, True, True, False], dtype=np.bool_)
      # pylint: disable=unexpected-keyword-arg
      # The `max_stack_depth` and `backend` keyword arguments to `even`
      # are introduced by the `@ab.batch` decorator, confusing pylint.
      self.assertAllEqual(
          outputs,
          self.evaluate(
              even(inputs, max_stack_depth=15, backend=TF_BACKEND)
          ),
      )
  
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 AutoGraphFrontendTest obj's underlying member method code with example usage: obj.testEvenOddWithContext() and returns: instructions, True, odd, False, even