code: def testPdfOfSampleMultiDims(self):
      d = exp_gamma_lib.ExpGamma(
          concentration=[7.0, 11.0],
          rate=[[5.0], [6.0]],
          validate_args=True,
      )
      num = 50000
      samples = d.sample(num, seed=test_util.test_seed())
      pdfs = d.prob(samples)
      sample_vals, pdf_vals = self.evaluate([samples, pdfs])
      self.assertEqual(samples.shape, (num, 2, 2))
      self.assertEqual(pdfs.shape, (num, 2, 2))
      self._assertIntegral(
          sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02
      )
      self._assertIntegral(
          sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02
      )
      self._assertIntegral(
          sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02
      )
      self._assertIntegral(
          sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02
      )
      self.assertAllClose(d.mean(), sample_vals.mean(axis=0), atol=0.1)
      self.assertAllClose(
          d.variance(), sample_vals.var(axis=0), atol=0.1
      )
  
imports: 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 exp_gamma
  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 test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 19 for calling the ExpGammaTest obj's underlying member method code with example usage: obj.testPdfOfSampleMultiDims() without return types