code: def make_fused_batch_norm_tests(options):
      """Make a set of tests to do fused_batch_norm."""
  
      test_parameters = [
          {
              "dtype": [tf.float32],
              "input_shape": [[1, 1, 6, 2]],
              "epsilon": [0.001, 0.1],
              "is_training": [False],
          },
          {
              "dtype": [tf.float32],
              "input_shape": [[1, 1, 6, 2]],
              "epsilon": [0.001, 0.1],
              "is_training": [True],
          },
          {
              "dtype": [tf.float32],
              "input_shape": [[1, None, 6, 2]],
              "epsilon": [0.001, 0.1],
              "is_training": [True, False],
          },
      ]
  
      def build_graph(parameters):
          """Build the testing graph for fused batch normalization."""
          input_shape = parameters["input_shape"]
          scale_shape = input_shape[3]
  
          scale = create_tensor_data(parameters["dtype"], scale_shape)
          offset = create_tensor_data(parameters["dtype"], scale_shape)
          mean = create_tensor_data(parameters["dtype"], scale_shape)
          variance = create_tensor_data(
              parameters["dtype"], scale_shape
          )
  
          x = tf.compat.v1.placeholder(
              dtype=parameters["dtype"],
              name="x",
              shape=parameters["input_shape"],
          )
          [x_norm, _, _] = tf.compat.v1.nn.fused_batch_norm(
              x,
              scale,
              offset,
              mean,
              variance,
              parameters["epsilon"],
              data_format="NHWC",
              is_training=parameters["is_training"],
          )
  
          input_tensor = tf.compat.v1.placeholder(
              dtype=parameters["dtype"],
              name="input",
              shape=parameters["input_shape"],
          )
          out = tf.add(input_tensor, x_norm)
          return [x, input_tensor], [out]
  
      def build_inputs(parameters, sess, inputs, outputs):
          # Fill dynamic shape with a random number.
          input_shape = parameters["input_shape"]
          input_shape = [
              np.random.randint(1, 10) if v is None else v
              for v in input_shape
          ]
  
          input_values = [
              create_tensor_data(parameters["dtype"], input_shape),
              create_tensor_data(parameters["dtype"], input_shape),
          ]
  
          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 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_fused_batch_norm_tests(options) and returns: list,  list, input_values,  sess