code: def make_arg_min_max_tests(options):
      """Make a set of tests to do arg_max."""
  
      test_parameters = [
          {
              "input_dtype": [tf.float32, tf.int32],
              "input_shape": [
                  [],
                  [1, 1, 1, 3],
                  [2, 3, 4, 5],
                  [2, 3, 3],
                  [5, 5],
                  [10],
              ],
              "output_type": [tf.int32, tf.int64],
              "is_arg_max": [True],
              "is_last_axis": [False],
              "dynamic_range_quantize": [False, True],
          },
          {
              "input_dtype": [tf.float32, tf.int32],
              "input_shape": [
                  [1],
                  [2],
                  [3],
                  [4],
                  [5],
                  [6],
                  [7],
                  [8],
                  [9],
                  [10],
                  [2, 10],
                  [3, 4, 50],
                  [2, 3, 5, 100],
              ],
              "output_type": [tf.int32, tf.int64],
              "is_arg_max": [False, True],
              "is_last_axis": [True],
              "dynamic_range_quantize": [False, True],
          },
          {
              "input_dtype": [tf.bool],
              "input_shape": [
                  [1, 1, 1, 3],
                  [2, 3, 4, 5],
                  [2, 3, 3],
                  [5, 5],
                  [10],
              ],
              "output_type": [tf.int32, tf.int64],
              "is_arg_max": [True],
              "is_last_axis": [False],
          },
      ]
  
      def build_graph(parameters):
          """Build the topk op testing graph."""
          input_value = tf.compat.v1.placeholder(
              dtype=parameters["input_dtype"],
              name="input",
              shape=parameters["input_shape"],
          )
          if not parameters["is_last_axis"]:
              axis = random.randint(
                  0, max(len(parameters["input_shape"]) - 1, 0)
              )
          else:
              axis = -1
          if parameters["is_arg_max"]:
              out = tf.math.argmax(
                  input=input_value,
                  axis=axis,
                  output_type=parameters["output_type"],
              )
          else:
              out = tf.math.argmin(
                  input=input_value,
                  axis=axis,
                  output_type=parameters["output_type"],
              )
          return [input_value], [out]
  
      def build_inputs(parameters, sess, inputs, outputs):
          input_value = create_tensor_data(
              parameters["input_dtype"], parameters["input_shape"]
          )
          return [input_value], sess.run(
              outputs, feed_dict=dict(zip(inputs, [input_value]))
          )
  
      make_zip_of_tests(
          options,
          test_parameters,
          build_graph,
          build_inputs,
          expected_tf_failures=8,
      )
  
imports: import random
  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_arg_min_max_tests(options) and returns: list,  list, list,  sess