code: def test_interpolation(self):
      dtype = tf.float64
      interval_times = tf.constant(
          [0.25, 0.5, 1.0, 2.0, 3.0, 4.0], dtype=dtype
      )
      interval_values = tf.constant(
          [0.05, 0.051, 0.052, 0.053, 0.055, 0.055], dtype=dtype
      )
      test_times = tf.constant(
          [0.25, 0.5, 1.0, 2.0, 3.0, 4.0, 1.1], dtype=dtype
      )
      expected = [
          0.0505,
          0.05133333,
          0.05233333,
          0.055,
          0.055,
          0.055,
          0.05241,
      ]
      actual, integrated_actual = self.evaluate(
          monotone_convex.interpolate(
              test_times, interval_values, interval_times
          )
      )
      np.testing.assert_allclose(actual, expected)
      integrated_expected = [0, 0, 0, 0, 0, 0.055, 0.005237]
      np.testing.assert_allclose(integrated_actual, integrated_expected)
  
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 2 for calling the MonotoneConvexTest obj's underlying member method code with example usage: obj.test_interpolation() without return types