code: def make_conv3d_transpose_tests(options):
      """Make a set of tests to do conv3d_transpose."""
  
      test_parameters = [
          {
              "shape_dtype": [tf.int32, tf.int64],
              "input_dtype": [tf.float32],
              "input_shape": [[2, 3, 4, 5, 2], [2, 5, 6, 8, 2]],
              "filter_shape": [[2, 2, 2, 3, 2], [1, 2, 2, 3, 2]],
              "strides": [
                  (1, 1, 1, 1, 1),
                  (1, 1, 1, 2, 1),
                  (1, 1, 2, 2, 1),
                  (1, 2, 1, 2, 1),
                  (1, 2, 2, 2, 1),
              ],
              "dilations": [(1, 1, 1, 1, 1)],
              "padding": ["SAME", "VALID"],
          }
      ]
  
      def build_graph(parameters):
          """Build the exp op testing graph."""
          output_shape = tf.compat.v1.placeholder(
              dtype=parameters["shape_dtype"], name="input", shape=[5]
          )
          input_tensor = tf.compat.v1.placeholder(
              dtype=parameters["input_dtype"],
              name="input",
              shape=parameters["input_shape"],
          )
          filter_tensor = tf.compat.v1.placeholder(
              dtype=parameters["input_dtype"],
              name="filter",
              shape=parameters["filter_shape"],
          )
  
          out = tf.nn.conv3d_transpose(
              input_tensor,
              filter_tensor,
              output_shape,
              strides=parameters["strides"],
              dilations=parameters["dilations"],
              padding=parameters["padding"],
          )
          return [input_tensor, filter_tensor, output_shape], [out]
  
      def calculate_output_shape(parameters):
          def calculate_shape(idx):
              input_size = parameters["input_shape"][idx]
              filter_size = parameters["filter_shape"][idx - 1]
              stride = parameters["strides"][idx]
              if parameters["padding"] == "SAME":
                  return (input_size - 1) * stride + 1
              else:
                  return (input_size - 1) * stride + filter_size
  
          output_shape_values = [parameters["input_shape"][0]]
          output_shape_values.append(calculate_shape(1))
          output_shape_values.append(calculate_shape(2))
          output_shape_values.append(calculate_shape(3))
          output_shape_values.append(parameters["filter_shape"][3])
          return np.dtype(
              parameters["shape_dtype"].as_numpy_dtype()
          ).type(output_shape_values)
  
      def build_inputs(parameters, sess, inputs, outputs):
          values = [
              create_tensor_data(
                  parameters["input_dtype"],
                  parameters["input_shape"],
                  min_value=-100,
                  max_value=9,
              ),
              create_tensor_data(
                  parameters["input_dtype"],
                  parameters["filter_shape"],
                  min_value=-3,
                  max_value=3,
              ),
              calculate_output_shape(parameters),
          ]
          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_conv3d_transpose_tests(options) and returns: list,  list, np, values,  sess, unknown, unknown