code: def testBatchDimensionSensitivePrimop(self):
      def batchwise_reduce_sum(x):
          return tf.reduce_sum(
              input_tensor=x, axis=tf.range(1, tf.rank(x))
          )
  
      def my_type(args):
          return instructions.TensorType(args[0].dtype, ())
  
      def function(x):
          y = batchwise_reduce_sum(x)
          return y + y
  
      batched = frontend.Context().batch_uncurried(function, my_type)
      inputs = np.array([[12, 13], [-13, 10], [14, 1]], dtype=np.int32)
      self.assertAllEqual(
          [50, -6, 30],
          self.evaluate(
              batched(inputs, max_stack_depth=5, 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 14 for calling the AutoGraphFrontendTest obj's underlying member method code with example usage: obj.testBatchDimensionSensitivePrimop() and returns: tf, instructions, unknown