code: def make_reshape_tests(options):
      """Make a set of tests to do reshape."""
  
      # All shapes below are suitable for tensors with 420 elements.
      test_parameters = [
          {
              "dtype": [tf.float32, tf.int32],
              "input_shape": [
                  [3, 4, 5, 7],
                  [4, 105],
                  [21, 5, 2, 2],
                  [420],
              ],
              "output_shape": [[15, 28], [420], [1, -1, 5, 7], [-1]],
              "constant_shape": [True, False],
              "fully_quantize": [False],
          },
          {
              "dtype": [tf.float32],
              "input_shape": [[1]],
              "output_shape": [[]],
              "constant_shape": [True, False],
              "fully_quantize": [False],
          },
          {
              "dtype": [tf.float32],
              "input_shape": [
                  [3, 4, 5, 7],
                  [4, 105],
                  [21, 5, 2, 2],
                  [420],
              ],
              "output_shape": [[15, 28], [420], [1, -1, 5, 7], [-1]],
              "constant_shape": [True],
              "fully_quantize": [True],
          },
          {
              # Zero in input shape.
              "dtype": [tf.float32],
              "input_shape": [[1, 4, 0]],
              "output_shape": [[2, -1], [2, 0, -1]],
              "constant_shape": [True, False],
              "fully_quantize": [False],
          },
      ]
  
      def build_graph(parameters):
          """Build the graph for reshape tests."""
          input_tensor = tf.compat.v1.placeholder(
              dtype=parameters["dtype"],
              name="input",
              shape=parameters["input_shape"],
          )
  
          # Get shape as either a placeholder or constants.
          if parameters["constant_shape"]:
              output_shape = parameters["output_shape"]
              input_tensors = [input_tensor]
          else:
              # The shape of the shape tensor.
              shape_tensor_shape = [len(parameters["output_shape"])]
              output_shape = tf.compat.v1.placeholder(
                  dtype=tf.int32,
                  name="output_shape",
                  shape=shape_tensor_shape,
              )
              input_tensors = [input_tensor, output_shape]
          out = tf.reshape(input_tensor, shape=output_shape)
          return input_tensors, [out]
  
      def build_inputs(parameters, sess, inputs, outputs):
          """Build inputs for reshape op."""
  
          values = [
              create_tensor_data(
                  parameters["dtype"],
                  parameters["input_shape"],
                  min_value=-1,
                  max_value=1,
              )
          ]
          if not parameters["constant_shape"]:
              values.append(np.array(parameters["output_shape"]))
  
          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_reshape_tests(options) and returns: input_tensors,  list, values,  sess