code: def make_transpose_tests(options):
      """Make a set of tests to do transpose."""
  
      # TODO(nupurgarg): Add test for uint8.
      test_parameters = [
          {
              "dtype": [tf.int32, tf.int64, tf.float32],
              "input_shape": [[2, 2, 3]],
              "perm": [[0, 1, 2], [0, 2, 1]],
              "constant_perm": [True, False],
              "fully_quantize": [False],
          },
          {
              "dtype": [tf.float32],
              "input_shape": [[1, 2, 3, 4]],
              "perm": [[0, 1, 2, 3], [3, 0, 1, 2]],
              "constant_perm": [True, False],
              "fully_quantize": [False],
          },
          {
              "dtype": [tf.float32],
              "input_shape": [[1, 2, 3, 4, 5]],
              "perm": [[4, 3, 2, 1, 0]],
              "constant_perm": [True, False],
              "fully_quantize": [False],
          },
          {
              "dtype": [tf.float32],
              "input_shape": [[2, 2, 3]],
              "perm": [[0, 1, 2], [0, 2, 1]],
              "constant_perm": [True],
              "fully_quantize": [True],
          },
          {
              "dtype": [tf.float32],
              "input_shape": [[1, 2, 3, 4]],
              "perm": [[0, 1, 2, 3], [3, 0, 1, 2]],
              "constant_perm": [True],
              "fully_quantize": [True],
          },
          {
              "dtype": [tf.float32],
              "input_shape": [[1, 2, 3, 4, 5]],
              "perm": [[0, 1, 2, 3, 4], [3, 4, 0, 1, 2]],
              "constant_perm": [True],
              "fully_quantize": [True, False],
          },
          {
              "dtype": [tf.float32],
              "input_shape": [[2, 2]],
              "perm": [[-2, -1]],
              "constant_perm": [True, False],
              "fully_quantize": [False],
          },
      ]
  
      def build_graph(parameters):
          """Build a transpose graph given `parameters`."""
          input_tensor = tf.compat.v1.placeholder(
              dtype=parameters["dtype"],
              name="input",
              shape=parameters["input_shape"],
          )
  
          if parameters["constant_perm"]:
              perm = parameters["perm"]
              input_tensors = [input_tensor]
          else:
              shape = len(parameters["perm"])
              perm = tf.compat.v1.placeholder(
                  dtype=tf.int32, name="perm", shape=shape
              )
              input_tensors = [input_tensor, perm]
  
          out = tf.transpose(a=input_tensor, perm=perm)
          return input_tensors, [out]
  
      def build_inputs(parameters, sess, inputs, outputs):
          values = [
              create_tensor_data(
                  parameters["dtype"],
                  parameters["input_shape"],
                  min_value=-1,
                  max_value=1,
              )
          ]
          if not parameters["constant_perm"]:
              values.append(np.array(parameters["perm"]))
          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_transpose_tests(options) and returns: input_tensors,  list, values,  sess