code: def testBroadcastInputsToBatchSize(self):
      # The desired batch size is inferred from the inputs.  This test is checking
      # that the system supports broadcasting (other) inputs across the batch
      # dimension, which requires not accidentally inferring a batch size of 1.
      def my_type(args):
          return args[0]
  
      def function(a, b, c, d, e, f, g):
          return a + b + c + d + e + f + g
  
      a_in = np.array([12, 13, 14], dtype=np.int32)
      b_in = np.array([2], dtype=np.int32)
      c_in = np.array([3], dtype=np.int32)
      d_in = np.array([4], dtype=np.int32)
      ctx = frontend.Context()
      batched = ctx.batch_uncurried(function, my_type)
      # Repeat several times, because the buggy behavior this is catching
      # depended on Python dict order traversal.
      for _ in range(10):
          self.assertAllEqual(
              [39, 40, 41],
              batched(
                  a_in,
                  b_in,
                  c_in,
                  d_in,
                  5,
                  6,
                  7,
                  max_stack_depth=5,
                  backend=NP_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 17 for calling the AutoGraphFrontendTest obj's underlying member method code with example usage: obj.testBroadcastInputsToBatchSize() and returns: args, unknown