code: def testSurvivalFunction(self):
      dist = self.make_two_piece_student_ts()
  
      x = np.array(
          [[-123.0], [-4.0], [3.0], [10.0], [88.0]], dtype=self.dtype
      )
  
      sf = self.evaluate(dist.survival_function(x))
      # The following values were calculated using the following relation:
      #   expected_sf = 1 - expected_cdf
      expected_sf = np.array(
          [
              [
                  9.999488670863345e-01,
                  9.999923486112747e-01,
                  9.999989587458707e-01,
              ],
              [
                  6.042185357523240e-01,
                  7.449283095246200e-01,
                  8.599516481827990e-01,
              ],
              [
                  3.600000000000000e-01,
                  5.000000000000000e-01,
                  6.388457510202610e-01,
              ],
              [
                  1.392002398118740e-01,
                  2.550716904753800e-01,
                  3.945180366559990e-01,
              ],
              [
                  1.017145781800899e-05,
                  7.256411207701152e-05,
                  4.413392195470323e-04,
              ],
          ],
          dtype=self.dtype,
      )
  
      self.assertDTypeEqual(sf, self.dtype)
      self.assertShapeEqual(sf, expected_sf)
      self.assertAllClose(
          sf,
          expected_sf,
          atol=0.0,
          rtol=1e-6 if self.dtype == np.float32 else 5e-11,
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import two_piece_student_t
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 11 for calling the _TwoPieceStudentTTest obj's underlying member method code with example usage: obj.testSurvivalFunction() without return types