code: def make_concat_tests(options):
      """Make a set of tests to do concatenation."""
  
      test_parameters = [
          {
              "base_shape": [[1, 3, 4, 3], [3, 4]],
              "num_tensors": [1, 2, 3, 4, 5, 6],
              "axis": [0, 1, 2, 3, -3, -2, -1],
              "type": [tf.float32, tf.uint8, tf.int32, tf.int64],
              "fully_quantize": [False],
              "quant_16x8": [False],
              "dynamic_range_quantize": [False],
          },
          {
              "base_shape": [[1, 3, 4, 3], [3, 4], [2, 3, 4, 3]],
              "num_tensors": [1, 2, 3, 4, 5, 6],
              "axis": [1, 2, 3, -3, -2, -1],
              "type": [tf.float32],
              "fully_quantize": [True],
              "quant_16x8": [False],
              "dynamic_range_quantize": [False],
          },
          {
              "base_shape": [[1, 3, 4, 3]],
              "num_tensors": [6],
              "axis": [-1],
              "type": [tf.float32],
              "fully_quantize": [True],
              "quant_16x8": [True],
              "dynamic_range_quantize": [False],
          },
          {
              "base_shape": [[1, 3, 4, 3]],
              "num_tensors": [6],
              "axis": [1],
              "type": [tf.float32],
              "fully_quantize": [False],
              "quant_16x8": [False],
              "dynamic_range_quantize": [True],
          },
          {
              "base_shape": [[1, 3, 4, 3]],
              "num_tensors": [6],
              "axis": [1],
              "type": [tf.bool],
              "fully_quantize": [False],
              "quant_16x8": [False],
              "dynamic_range_quantize": [True],
          },
      ]
  
      def get_shape(parameters, delta):
          """Return a tweaked version of 'base_shape'."""
          axis = parameters["axis"]
          shape = parameters["base_shape"][:]
          if axis < 0:
              axis += len(shape)
          if axis < len(shape):
              shape[axis] += delta
          return shape
  
      def build_graph(parameters):
          all_tensors = []
          for n in range(0, parameters["num_tensors"]):
              input_tensor = tf.compat.v1.placeholder(
                  dtype=parameters["type"],
                  name=("input%d" % n),
                  shape=get_shape(parameters, n),
              )
              all_tensors.append(input_tensor)
          out = tf.concat(all_tensors, parameters["axis"])
          return all_tensors, [out]
  
      def build_inputs(parameters, sess, inputs, outputs):
          all_values = []
          for n in range(0, parameters["num_tensors"]):
              input_values = create_tensor_data(
                  parameters["type"],
                  get_shape(parameters, n),
                  min_value=-1,
                  max_value=1,
              )
              all_values.append(input_values)
          return all_values, sess.run(
              outputs, feed_dict=dict(zip(inputs, all_values))
          )
  
      make_zip_of_tests(
          options,
          test_parameters,
          build_graph,
          build_inputs,
          expected_tf_failures=75,
      )
  
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_concat_tests(options) and returns: shape, all_tensors,  list, all_values,  sess