code: def testVonMisesSampleExtremeConcentration(self):
      loc = self.make_tensor([1.0, np.nan, 1.0, 1.0, np.nan])
      min_value = np.finfo(dtype_util.as_numpy_dtype(self.dtype)).min
      max_value = np.finfo(dtype_util.as_numpy_dtype(self.dtype)).max
      concentration = self.make_tensor(
          [min_value, 1.0, max_value, np.nan, np.nan]
      )
      dist = von_mises.VonMises(loc, concentration, validate_args=False)
  
      samples = dist.sample(seed=test_util.test_seed())
      # Check that it does not end up in an infinite loop.
      self.assertEqual(self.evaluate(samples).shape, (5,))
  
imports: import numpy
  from scipy import special
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import von_mises
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 25 for calling the _VonMisesTest obj's underlying member method code with example usage: obj.testVonMisesSampleExtremeConcentration() without return types