code: def make_pad_tests(options):
      """Make a set of tests to do pad."""
  
      # TODO(nupurgarg): Add test for tf.uint8.
      test_parameters = [
          # 5D:
          {
              "dtype": [tf.int32, tf.int64, tf.float32],
              "input_shape": [[1, 1, 2, 1, 1], [2, 1, 1, 1, 1]],
              "padding_dtype": [tf.int32, tf.int64],
              "paddings": [
                  [[0, 0], [0, 1], [2, 3], [0, 0], [1, 0]],
                  [[0, 1], [0, 0], [0, 0], [2, 3], [1, 0]],
              ],
              "constant_paddings": [True, False],
              "fully_quantize": [False],
              "quant_16x8": [False],
          },
          # 4D:
          {
              "dtype": [tf.int32, tf.int64, tf.float32],
              "input_shape": [[1, 1, 2, 1], [2, 1, 1, 1]],
              "padding_dtype": [tf.int32, tf.int64],
              "paddings": [
                  [[0, 0], [0, 1], [2, 3], [0, 0]],
                  [[0, 1], [0, 0], [0, 0], [2, 3]],
              ],
              "constant_paddings": [True, False],
              "fully_quantize": [False],
              "quant_16x8": [False],
          },
          # 2D:
          {
              "dtype": [tf.int32, tf.int64, tf.float32],
              "input_shape": [[1, 2]],
              "padding_dtype": [tf.int32, tf.int64],
              "paddings": [[[0, 1], [2, 3]]],
              "constant_paddings": [True, False],
              "fully_quantize": [False],
              "quant_16x8": [False],
          },
          # 1D:
          {
              "dtype": [tf.int32],
              "input_shape": [[1]],
              "padding_dtype": [tf.int32, tf.int64],
              "paddings": [[[1, 2]]],
              "constant_paddings": [False],
              "fully_quantize": [False],
              "quant_16x8": [False],
          },
          # 4D:
          {
              "dtype": [tf.float32],
              "input_shape": [[1, 1, 2, 1], [2, 1, 1, 1]],
              "padding_dtype": [tf.int32, tf.int64],
              "paddings": [
                  [[0, 0], [0, 1], [2, 3], [0, 0]],
                  [[0, 1], [0, 0], [0, 0], [2, 3]],
                  [[0, 0], [0, 0], [0, 0], [0, 0]],
              ],
              "constant_paddings": [True],
              "fully_quantize": [True],
              "quant_16x8": [False, True],
          },
          # 2D:
          {
              "dtype": [tf.float32],
              "input_shape": [[1, 2]],
              "padding_dtype": [tf.int32, tf.int64],
              "paddings": [[[0, 1], [2, 3]]],
              "constant_paddings": [True],
              "fully_quantize": [True],
              "quant_16x8": [False, True],
          },
          # 1D:
          {
              "dtype": [tf.float32],
              "input_shape": [[1]],
              "padding_dtype": [tf.int32, tf.int64],
              "paddings": [[[1, 2]]],
              "constant_paddings": [True],
              "fully_quantize": [True],
              "quant_16x8": [False, True],
          },
      ]
  
      def build_graph(parameters):
          """Build a pad graph given `parameters`."""
          input_tensor = tf.compat.v1.placeholder(
              dtype=parameters["dtype"],
              name="input",
              shape=parameters["input_shape"],
          )
  
          # Get paddings as either a placeholder or constants.
          if parameters["constant_paddings"]:
              paddings = parameters["paddings"]
              input_tensors = [input_tensor]
          else:
              shape = [len(parameters["paddings"]), 2]
              paddings = tf.compat.v1.placeholder(
                  dtype=parameters["padding_dtype"],
                  name="padding",
                  shape=shape,
              )
              input_tensors = [input_tensor, paddings]
  
          out = tf.pad(tensor=input_tensor, paddings=paddings)
          return input_tensors, [out]
  
      def build_inputs(parameters, sess, inputs, outputs):
          """Build inputs for pad op."""
  
          values = [
              create_tensor_data(
                  parameters["dtype"],
                  parameters["input_shape"],
                  min_value=-1,
                  max_value=1,
              )
          ]
          if not parameters["constant_paddings"]:
              values.append(np.array(parameters["paddings"]))
          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_pad_tests(options) and returns: input_tensors,  list, values,  sess