code: def test_log1psquare(self, x, expected_y, expected_dydx):
      x = tf.convert_to_tensor(x, dtype=self.dtype)
      if NUMPY_MODE:
          y = numeric.log1psquare(x)
          dydx, expected_dydx = np.zeros([]), np.zeros([])
      else:
          y, dydx = gradient.value_and_gradient(numeric.log1psquare, x)
      y_, dydx_ = self.evaluate([y, dydx])
      self.assertAllClose(expected_y, y_)
      self.assertAllClose(expected_dydx, dydx_)
  
file_path: probability/tensorflow_probability/python/math/numeric_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math import numeric
label_desc: Use image node_id 1 for calling the Log1pSquareTest32 obj's underlying member method code with example usage: obj.test_log1psquare(x, expected_y, expected_dydx) without return types