code: def make_scatter_nd_tests(options):
      """Make a set of tests to do scatter_nd."""
  
      test_parameters = [
          {
              "indices_dtype": [tf.int32],
              "indices_shape": [[4, 1]],
              "indices_value": [[[4], [3], [1], [7]]],
              "updates_dtype": [
                  tf.int32,
                  tf.int64,
                  tf.float32,
                  tf.bool,
              ],
              "updates_shape": [[4]],
              "shape_dtype": [tf.int32],
              "shape_shape": [[1]],
              "shape_value": [[8]],
          },
          {
              "indices_dtype": [tf.int32],
              "indices_shape": [[4, 2]],
              "indices_value": [[[0, 0], [1, 0], [0, 2], [1, 2]]],
              "updates_dtype": [
                  tf.int32,
                  tf.int64,
                  tf.float32,
                  tf.bool,
              ],
              "updates_shape": [[4, 5]],
              "shape_dtype": [tf.int32],
              "shape_shape": [[3]],
              "shape_value": [[2, 3, 5]],
          },
      ]
  
      def build_graph(parameters):
          """Build the scatter_nd op testing graph."""
          indices = tf.compat.v1.placeholder(
              dtype=parameters["indices_dtype"],
              name="indices",
              shape=parameters["indices_shape"],
          )
          updates = tf.compat.v1.placeholder(
              dtype=parameters["updates_dtype"],
              name="updates",
              shape=parameters["updates_shape"],
          )
          shape = tf.compat.v1.placeholder(
              dtype=parameters["shape_dtype"],
              name="shape",
              shape=parameters["shape_shape"],
          )
          out = tf.scatter_nd(indices, updates, shape)
          return [indices, updates, shape], [out]
  
      def build_inputs(parameters, sess, inputs, outputs):
          indices = np.array(parameters["indices_value"])
          updates = create_tensor_data(
              parameters["updates_dtype"], parameters["updates_shape"]
          )
          shape = np.array(parameters["shape_value"])
          return [indices, updates, shape], sess.run(
              outputs,
              feed_dict=dict(zip(inputs, [indices, updates, shape])),
          )
  
      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_scatter_nd_tests(options) and returns: list,  list, list,  sess