code: def make_fully_connected_tests(options):
      """Make a set of tests to do fully_connected."""
  
      test_parameters = [
          {
              "shape1": [[3, 3]],
              "shape2": [[3, 3]],
              "transpose_a": [True, False],
              "transpose_b": [True, False],
              "constant_filter": [True, False],
              "fully_quantize": [False],
              "quant_16x8": [False],
          },
          {
              "shape1": [[4, 4], [1, 4], [4]],
              "shape2": [[4, 4], [4, 1], [4]],
              "transpose_a": [False],
              "transpose_b": [False],
              "constant_filter": [True, False],
              "fully_quantize": [False],
              "quant_16x8": [False],
          },
          {
              "shape1": [[40, 37]],
              "shape2": [[37, 40]],
              "transpose_a": [False],
              "transpose_b": [False],
              "constant_filter": [True, False],
              "fully_quantize": [False],
              "quant_16x8": [False],
          },
          {
              "shape1": [[40, 37]],
              "shape2": [[40, 37]],
              "transpose_a": [False],
              "transpose_b": [True],
              "constant_filter": [True, False],
              "fully_quantize": [False],
              "quant_16x8": [False],
          },
          {
              "shape1": [[5, 3]],
              "shape2": [[5, 3]],
              "transpose_a": [True],
              "transpose_b": [False],
              "constant_filter": [True, False],
              "fully_quantize": [False],
              "quant_16x8": [False],
          },
          {
              "shape1": [[1, 3]],
              "shape2": [[3, 3]],
              "transpose_a": [False],
              "transpose_b": [False],
              "constant_filter": [True],
              "fully_quantize": [True],
              "quant_16x8": [False],
          },
          {
              "shape1": [[1, 4], [4]],
              "shape2": [[4, 4], [4, 1], [4]],
              "transpose_a": [False],
              "transpose_b": [False],
              "constant_filter": [True],
              "fully_quantize": [True],
              "quant_16x8": [False],
          },
          {
              "shape1": [[1, 37], [2, 37]],
              "shape2": [[37, 40]],
              "transpose_a": [False],
              "transpose_b": [False],
              "constant_filter": [True],
              "fully_quantize": [True],
              "quant_16x8": [False],
          },
          {
              "shape1": [[1, 3], [2, 3]],
              "shape2": [[3, 5], [3, 1]],
              "transpose_a": [False],
              "transpose_b": [False],
              "constant_filter": [True],
              "fully_quantize": [True],
              "quant_16x8": [False],
          },
          {
              "shape1": [[2, 3]],
              "shape2": [[3, 5]],
              "transpose_a": [False],
              "transpose_b": [False],
              "constant_filter": [True],
              "fully_quantize": [True],
              "quant_16x8": [True],
          },
          {
              "shape1": [[0, 3]],
              "shape2": [[3, 3]],
              "transpose_a": [False],
              "transpose_b": [False],
              "constant_filter": [True, False],
              "fully_quantize": [False],
              "quant_16x8": [False],
          },
          {
              "shape1": [[3, 0]],
              "shape2": [[0, 3]],
              "transpose_a": [False],
              "transpose_b": [False],
              "constant_filter": [True, False],
              "fully_quantize": [False],
              "quant_16x8": [False],
          },
      ]
  
      def build_graph(parameters):
          """Build a matmul graph given `parameters`."""
          input_tensor1 = tf.compat.v1.placeholder(
              dtype=tf.float32,
              name="input1",
              shape=parameters["shape1"],
          )
  
          # Get input_tensor2 either as a placeholder or constants. Also get a list of
          # the input tensors that are represented as placeholders.
          if parameters["constant_filter"]:
              input_tensor2 = create_tensor_data(
                  np.float32,
                  parameters["shape2"],
                  min_value=-1,
                  max_value=1,
              )
              input_tensors = [input_tensor1]
          else:
              input_tensor2 = tf.compat.v1.placeholder(
                  dtype=tf.float32,
                  name="input2",
                  shape=parameters["shape2"],
              )
              input_tensors = [input_tensor1, input_tensor2]
  
          out = tf.matmul(
              input_tensor1,
              input_tensor2,
              transpose_a=parameters["transpose_a"],
              transpose_b=parameters["transpose_b"],
          )
          return input_tensors, [out]
  
      def build_inputs(parameters, sess, inputs, outputs):
          # pylint: disable=g-doc-return-or-yield, g-doc-args
          """Build list of input values.
  
          It either contains 1 tensor (input_values1) or
          2 tensors (input_values1, input_values2) based on whether the second input
          is a constant or variable input.
          """
  
          values = [
              create_tensor_data(
                  np.float32,
                  shape=parameters["shape1"],
                  min_value=-1,
                  max_value=1,
              )
          ]
          if not parameters["constant_filter"]:
              values.append(
                  create_tensor_data(
                      np.float32,
                      parameters["shape2"],
                      min_value=-1,
                      max_value=1,
                  )
              )
          return values, sess.run(
              outputs, feed_dict=dict(zip(inputs, values))
          )
  
      make_zip_of_tests(
          options,
          test_parameters,
          build_graph,
          build_inputs,
          expected_tf_failures=14,
      )
  
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_fully_connected_tests(options) and returns: input_tensors,  list, values,  sess