code: def testInverseGaussianLogCdfMultidimensional(self):
      batch_size = 6
      loc_v = 1.0
      concentration_v = [2.0, 4.0, 5.0]
      x_v = np.array([[6.0, 7.0, 9.2, 5.0, 6.0, 7.0]]).T
      dist = inverse_gaussian.InverseGaussian(
          self.make_tensor([[loc_v]] * batch_size),
          self.make_tensor([concentration_v] * batch_size),
          validate_args=True,
      )
  
      log_cdf = dist.log_cdf(self.make_tensor(x_v))
      if self.use_static_shape:
          self.assertEqual(log_cdf.shape, (6, 3))
      self.assertAllClose(
          self.evaluate(log_cdf),
          _scipy_invgauss(loc_v, np.array(concentration_v)).logcdf(x_v),
      )
  
      cdf = dist.cdf(self.make_tensor(x_v))
      if self.use_static_shape:
          self.assertEqual(cdf.shape, (6, 3))
      self.assertAllClose(
          self.evaluate(cdf),
          _scipy_invgauss(loc_v, np.array(concentration_v)).cdf(x_v),
      )
  
imports: import numpy
  from scipy import misc
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import inverse_gaussian
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 13 for calling the _InverseGaussianTest obj's underlying member method code with example usage: obj.testInverseGaussianLogCdfMultidimensional() without return types