code: def test_supply_missing_tensor_inputs(
      self,
      batch_size,
      dtype,
      type_spec_getter,
      tensor_type,
      inner_shape,
  ):
      test_case.skip_if_not_tf2("Tensorflow 2.x required.")
  
      @tf.function(
          input_signature=[
              {
                  "x_1": tf.TensorSpec([None], dtype=tf.int32),
                  "x_2": type_spec_getter(dtype),
              }
          ]
      )
      def foo(inputs):
          return inputs
  
      conc_fn = foo.get_concrete_function()
      # structured_input_signature is a tuple of (args, kwargs). [0][0] retrieves
      # the structure of the first arg, which for `foo` is `inputs`.
      structured_inputs = tf.nest.pack_sequence_as(
          conc_fn.structured_input_signature[0][0],
          conc_fn.inputs,
          expand_composites=True,
      )
      missing_keys = ["x_2"]
      result = tf2_utils.supply_missing_inputs(
          structured_inputs, batch_size, missing_keys
      )
  
      self.assertCountEqual(missing_keys, result.keys())
      self.assertIsInstance(result["x_2"], tensor_type)
      self.assertEqual(
          result["x_2"].shape.as_list(), [batch_size] + inner_shape
      )
      self.assertEqual(result["x_2"].dtype, dtype)
  
imports: import itertools
  import tensorflow
  from tensorflow_transform import tf2_utils
  from tensorflow_transform import test_case
label_desc: Use image node_id 2 for calling the TF2UtilsTest obj's underlying member method code with example usage: obj.test_supply_missing_tensor_inputs(batch_size, dtype, type_spec_getter, tensor_type, inner_shape) and returns: inputs