code: def testLaplaceSampleMultiDimensional(self):
      loc_v = np.array([np.arange(1, 101, dtype=np.float32)])  # 1 x 100
      scale_v = np.array(
          [np.arange(1, 11, dtype=np.float32)]
      ).T  # 10 x 1
      dist = laplace.Laplace(
          loc=loc_v, scale=scale_v, validate_args=True
      )
      n = 10000
      samples = dist.sample(n, seed=test_util.test_seed())
      sample_values = self.evaluate(samples)
      self.assertEqual(samples.shape, (n, 10, 100))
      self.assertEqual(sample_values.shape, (n, 10, 100))
      zeros = np.zeros_like(loc_v + scale_v)  # 10 x 100
      loc_bc = loc_v + zeros
      scale_bc = scale_v + zeros
      self.assertAllClose(
          sample_values.mean(axis=0),
          sp_stats.laplace.mean(loc_bc, scale=scale_bc),
          rtol=0.35,
          atol=0.0,
      )
      self.assertAllClose(
          sample_values.var(axis=0),
          sp_stats.laplace.var(loc_bc, scale=scale_bc),
          rtol=0.10,
          atol=0.0,
      )
      fails = 0
      trials = 0
      for ai, a in enumerate(np.reshape(loc_v, [-1])):
          for bi, b in enumerate(np.reshape(scale_v, [-1])):
              s = sample_values[:, bi, ai]
              trials += 1
              fails += 0 if self._kstest(a, b, s) else 1
      self.assertLess(fails, trials * 0.03)
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import laplace
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 16 for calling the LaplaceTest obj's underlying member method code with example usage: obj.testLaplaceSampleMultiDimensional() without return types