code: def make_conv2d_transpose_tests(options):
      """Make a set of tests to do transpose_conv."""
  
      test_parameters = [
          {
              "input_shape": [[1, 50, 54, 3]],
              "filter_shape": [
                  [1, 1, 8, 3],
                  [1, 2, 8, 3],
                  [1, 3, 8, 3],
                  [1, 4, 8, 3],
              ],
              "output_shape": [[1, 100, 108, 8]],
              "dynamic_output_shape": [True, False],
          },
          {
              "input_shape": [[1, 16, 1, 512]],
              "filter_shape": [[4, 1, 512, 512]],
              "output_shape": [[1, 32, 1, 512]],
              "dynamic_output_shape": [True, False],
          },
          {
              "input_shape": [[1, 128, 128, 1]],
              "filter_shape": [[4, 4, 1, 1]],
              "output_shape": [[1, 256, 256, 1]],
              "dynamic_output_shape": [True, False],
          },
      ]
  
      def build_graph(parameters):
          """Build a transpose_conv graph given `parameters`."""
          input_tensor = tf.compat.v1.placeholder(
              dtype=tf.float32,
              name="input",
              shape=parameters["input_shape"],
          )
  
          filter_tensor = tf.compat.v1.placeholder(
              dtype=tf.float32,
              name="filter",
              shape=parameters["filter_shape"],
          )
  
          input_tensors = [input_tensor, filter_tensor]
  
          if parameters["dynamic_output_shape"]:
              output_shape = tf.compat.v1.placeholder(
                  dtype=tf.int32, shape=[4]
              )
              input_tensors.append(output_shape)
          else:
              output_shape = parameters["output_shape"]
  
          out = tf.nn.conv2d_transpose(
              input_tensor,
              filter_tensor,
              output_shape=output_shape,
              padding="SAME",
              strides=(1, 2, 2, 1),
          )
  
          return input_tensors, [out]
  
      def build_inputs(parameters, sess, inputs, outputs):
          values = [
              create_tensor_data(np.float32, parameters["input_shape"]),
              create_tensor_data(
                  np.float32, parameters["filter_shape"]
              ),
          ]
          if parameters["dynamic_output_shape"]:
              values.append(np.array(parameters["output_shape"]))
  
          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_conv2d_transpose_tests(options) and returns: input_tensors,  list, values,  sess