code: def testGammaLogPDFBoundary(self):
      # When concentration = 1, we have an exponential distribution. Check that at
      # 0 we have finite log prob.
      rate = np.array([0.1, 0.5, 1.0, 2.0, 5.0, 10.0], dtype=np.float32)
      gamma = gamma_lib.Gamma(
          concentration=1.0, rate=rate, validate_args=True
      )
      log_pdf = gamma.log_prob(0.0)
      self.assertAllClose(np.log(rate), self.evaluate(log_pdf))
  
      gamma = gamma_lib.Gamma(
          concentration=[2.0, 4.0, 0.5], rate=6.0, validate_args=True
      )
      log_pdf = self.evaluate(gamma.log_prob(0.0))
      self.assertAllNegativeInf(log_pdf[:2])
      self.assertAllPositiveInf(log_pdf[2])
  
      pdf = self.evaluate(gamma.prob(0.0))
      self.assertAllPositiveInf(pdf[2])
      self.assertAllFinite(pdf[:2])
  
imports: import functools
  from absl.testing import parameterized
  import numpy
  from scipy import misc
  from scipy import special
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import implementation_selection
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 3 for calling the GammaTest obj's underlying member method code with example usage: obj.testGammaLogPDFBoundary() without return types