code: def make_expand_dims_tests(options):
      """Make a set of tests to do expand_dims."""
  
      test_parameters = [
          {
              "input_type": [tf.float32, tf.int32],
              "input_shape": [[5, 4], [1, 5, 4]],
              "axis_value": [0, 1, 2, -1, -2, -3],
              "constant_axis": [True, False],
              "fully_quantize": [False],
          },
          {
              "input_type": [tf.float32],
              "input_shape": [[5, 4], [1, 5, 4]],
              "axis_value": [0, 1, 2, -1, -2, -3],
              "constant_axis": [True],
              "fully_quantize": [True],
          },
      ]
  
      def build_graph(parameters):
          """Build the where op testing graph."""
          inputs = []
          input_value = tf.compat.v1.placeholder(
              dtype=parameters["input_type"],
              name="input",
              shape=parameters["input_shape"],
          )
          inputs.append(input_value)
  
          if parameters["constant_axis"]:
              axis_value = tf.constant(
                  parameters["axis_value"], dtype=tf.int32, shape=[1]
              )
          else:
              axis_value = tf.compat.v1.placeholder(
                  dtype=tf.int32, name="axis", shape=[1]
              )
              inputs.append(axis_value)
  
          out = tf.expand_dims(input_value, axis=axis_value)
          return inputs, [out]
  
      def build_inputs(parameters, sess, inputs, outputs):
          """Builds the inputs for expand_dims."""
          input_values = []
          input_values.append(
              create_tensor_data(
                  parameters["input_type"],
                  parameters["input_shape"],
                  min_value=-1,
                  max_value=1,
              )
          )
          if not parameters["constant_axis"]:
              input_values.append(
                  np.array([parameters["axis_value"]], dtype=np.int32)
              )
          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_expand_dims_tests(options) and returns: inputs,  list, input_values,  sess