code: def test_interpolated_yields_with_discrete_forwards(self):
      dtypes = [np.float32, np.float64]
      for dtype in dtypes:
          reference_times = tf.constant(
              [1.0, 2.0, 3.0, 4.0], dtype=dtype
          )
          discrete_forwards = tf.constant(
              [5, 4.5, 4.1, 5.5], dtype=dtype
          )
          test_times = tf.constant(
              [0.25, 0.5, 1.0, 2.0, 3.0, 1.1, 2.5, 2.9, 3.6, 4.0],
              dtype=dtype,
          )
          expected = np.array(
              [
                  5.1171875,
                  5.09375,
                  5.0,
                  4.75,
                  4.533333,
                  4.9746,
                  4.624082,
                  4.535422,
                  4.661777,
                  4.775,
              ],
              dtype=dtype,
          )
          actual = self.evaluate(
              monotone_convex.interpolate_yields(
                  test_times,
                  reference_times,
                  discrete_forwards=discrete_forwards,
              )
          )
          np.testing.assert_allclose(actual, expected, rtol=1e-5)
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow.python.framework import test_util
  from tf_quant_finance.rates.hagan_west import monotone_convex
label_desc: Use image node_id 9 for calling the MonotoneConvexTest obj's underlying member method code with example usage: obj.test_interpolated_yields_with_discrete_forwards() without return types