code: def make_less_equal_tests(options):
      """Make a set of tests to do less_equal."""
  
      test_parameters = [
          {
              "input_dtype": [tf.float32, tf.int32, tf.int64],
              "input_shape_pair": [
                  ([1, 1, 1, 3], [1, 1, 1, 3]),
                  ([2, 3, 4, 5], [2, 3, 4, 5]),
                  ([2, 3, 3], [2, 3]),
                  ([5, 5], [1]),
                  ([10], [2, 4, 10]),
              ],
              "fully_quantize": [False],
          },
          {
              "input_dtype": [tf.float32],
              "input_shape_pair": [
                  ([1, 1, 1, 3], [1, 1, 1, 3]),
                  ([2, 3, 3], [2, 3]),
              ],
              "fully_quantize": [True],
          },
      ]
  
      # High dimension broadcasting support in MLIR converter.
      # Note(b/204360746): XNNPack delegate don't support high dimension.
      if not options.skip_high_dimension_inputs:
          test_parameters = test_parameters + [
              {
                  "input_dtype": [tf.float32, tf.int32],
                  "input_shape_pair": [
                      ([6, 5, 4, 3, 2, 1], [4, 3, 2, 1]),
                      ([6, 5, 4, 3, 2, 1], [None, 3, 2, 1]),
                      ([6, 5, None, 3, 2, 1], [None, 3, 2, 1]),
                  ],
                  "fully_quantize": [False],
                  "dynamic_size_value": [4, 1],
              },
          ]
  
      def populate_dynamic_shape(parameters, input_shape):
          return [
              parameters["dynamic_size_value"] if x is None else x
              for x in input_shape
          ]
  
      def build_graph(parameters):
          """Build the less_equal op testing graph."""
          input_value1 = tf.compat.v1.placeholder(
              dtype=parameters["input_dtype"],
              name="input1",
              shape=parameters["input_shape_pair"][0],
          )
          input_value2 = tf.compat.v1.placeholder(
              dtype=parameters["input_dtype"],
              name="input2",
              shape=parameters["input_shape_pair"][1],
          )
          out = tf.less_equal(input_value1, input_value2)
          return [input_value1, input_value2], [out]
  
      def build_inputs(parameters, sess, inputs, outputs):
          input_shape_1 = populate_dynamic_shape(
              parameters, parameters["input_shape_pair"][0]
          )
          input_shape_2 = populate_dynamic_shape(
              parameters, parameters["input_shape_pair"][1]
          )
  
          input_value1 = create_tensor_data(
              parameters["input_dtype"], input_shape_1
          )
          input_value2 = create_tensor_data(
              parameters["input_dtype"], input_shape_2
          )
          return [input_value1, input_value2], sess.run(
              outputs,
              feed_dict=dict(zip(inputs, [input_value1, input_value2])),
          )
  
      make_zip_of_tests(
          options,
          test_parameters,
          build_graph,
          build_inputs,
          expected_tf_failures=4,
      )
  
imports: 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_less_equal_tests(options) and returns: unknown, list,  list, list,  sess