code: def testLogPdf(self):
      # Keep the `concentration`'s above 1 since quadrature has problems
      # otherwise.
      a = np.array([3.0, 2.0, 8.0], dtype=self.dtype)[..., np.newaxis]
      b = np.array([1.8, 2.4, 3.2], dtype=self.dtype)[..., np.newaxis]
      c = np.array([5.5, 2.0, 4.3], dtype=self.dtype)[..., np.newaxis]
      d = np.array([1.6, 2.9, 6.4], dtype=self.dtype)[..., np.newaxis]
      dist = beta_quotient.BetaQuotient(a, b, c, d, validate_args=True)
      x = np.linspace(0.1, 10.0, 50).astype(self.dtype)
  
      self.assertAllClose(
          self._compute_logpdf_quadrature(a, b, c, d, x),
          self.evaluate(dist.log_prob(x)),
          rtol=1e-4,
      )
  
imports: import numpy
  from scipy import special
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import beta_quotient
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 5 for calling the _BetaQuotientTest obj's underlying member method code with example usage: obj.testLogPdf() without return types