code: def make_tensor_scatter_add_tests(options):
      """Make a set of tests to do tensor_scatter_add."""
  
      test_parameters = [
          {
              "input_dtype": [tf.float32, tf.int32, tf.int64],
              "input_shape": [[14], [2, 4, 7]],
              "adds_count": [1, 3, 5],
          }
      ]
  
      def build_graph(parameters):
          """Build the tensor_scatter_add op testing graph."""
          input_tensor = tf.compat.v1.placeholder(
              dtype=parameters["input_dtype"],
              name="input",
              shape=parameters["input_shape"],
          )
          # The indices will be a list of "input_shape".
          indices_tensor = tf.compat.v1.placeholder(
              dtype=tf.int32,
              name="indices",
              shape=(
                  [
                      parameters["adds_count"],
                      len(parameters["input_shape"]),
                  ]
              ),
          )
          # The adds will be a list of scalar, shaped of "adds_count".
          adds_tensors = tf.compat.v1.placeholder(
              dtype=parameters["input_dtype"],
              name="updates",
              shape=[parameters["adds_count"]],
          )
  
          out = tf.tensor_scatter_nd_add(
              input_tensor, indices_tensor, adds_tensors
          )
          return [input_tensor, indices_tensor, adds_tensors], [out]
  
      def build_inputs(parameters, sess, inputs, outputs):
          indices = set()
          while len(indices) < parameters["adds_count"]:
              loc = []
              for d in parameters["input_shape"]:
                  loc.append(np.random.randint(0, d))
              indices.add(tuple(loc))
  
          values = [
              create_tensor_data(
                  parameters["input_dtype"], parameters["input_shape"]
              ),
              np.array(list(indices), dtype=np.int32),
              create_tensor_data(
                  parameters["input_dtype"],
                  parameters["adds_count"],
                  min_value=-3,
                  max_value=3,
              ),
          ]
          return values, sess.run(
              outputs, feed_dict=dict(zip(inputs, values))
          )
  
      make_zip_of_tests(
          options, test_parameters, build_graph, build_inputs
      )
  
imports: import numpy
  import tensorflow
  from tensorflow.lite.testing.zip_test_utils import create_tensor_data
  from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
  from tensorflow.lite.testing.zip_test_utils import register_make_test_function
label_desc: Use image node_id 1 for calling a global function with example usage: make_tensor_scatter_add_tests(options) and returns: list,  list, values,  sess