code: def make_nearest_upsample_tests(options):
      """Make a set of tests to do nearest_upsample."""
  
      # Chose a set of parameters
      test_parameters = [
          {
              "input_shape": [[1, 10, 10, 64], [3, 8, 32]],
              "scale_n_axis": [
                  ([2, 2], [1, 2]),
                  ([3, 4], [1, 2]),
                  ([3], [1]),
              ],
              "dtype": [tf.float32, tf.int32],
          }
      ]
  
      def new_shape_for_upsample(original_shape, scales, axis):
          """Calculate the input shape & ones shape, also the upsample shape."""
          input_new_shape = []
          ones_new_shape = []
          upsample_new_shape = []
          j = 0
          for i in range(len(original_shape)):
              input_new_shape.append(original_shape[i])
              ones_new_shape.append(1)
              if j < len(scales) and axis[j] == i:
                  input_new_shape.append(1)
                  ones_new_shape.append(scales[j])
                  upsample_new_shape.append(
                      original_shape[i] * scales[j]
                  )
                  j += 1
              else:
                  upsample_new_shape.append(original_shape[i])
          return input_new_shape, ones_new_shape, upsample_new_shape
  
      def build_graph(parameters):
          """Build the nearest upsample testing graph."""
          input_shape = parameters["input_shape"]
          input_tensor = tf.compat.v1.placeholder(
              dtype=parameters["dtype"], name="input", shape=input_shape
          )
          scales, axis = parameters["scale_n_axis"]
          (
              input_new_shape,
              ones_new_shape,
              new_shape,
          ) = new_shape_for_upsample(input_shape, scales, axis)
  
          out = tf.compat.v1.reshape(
              input_tensor, input_new_shape
          ) * tf.compat.v1.ones(
              ones_new_shape, dtype=parameters["dtype"]
          )
          out = tf.compat.v1.reshape(out, new_shape)
          return [input_tensor], [out]
  
      def build_inputs(parameters, sess, inputs, outputs):
          input_values = create_tensor_data(
              parameters["dtype"],
              parameters["input_shape"],
              min_value=-10,
              max_value=10,
          )
          return [input_values], sess.run(
              outputs, feed_dict=dict(zip(inputs, [input_values]))
          )
  
      make_zip_of_tests(
          options, test_parameters, build_graph, build_inputs
      )
  
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_nearest_upsample_tests(options) and returns: input_new_shape,  ones_new_shape,  upsample_new_shape, list,  list, list,  sess