class_docstr: The von Mises distribution over angles.
  
  The von Mises distribution is a univariate directional distribution.
  Similarly to Normal distribution, it is a maximum entropy distribution.
  The samples of this distribution are angles, measured in radians.
  They are 2 pi-periodic: x = 0 and x = 2pi are equivalent.
  This means that the density is also 2 pi-periodic.
  The generated samples, however, are guaranteed to be in [-pi, pi) range.
  
  When `concentration = 0`, this distribution becomes a Uniform distribuion on
  the [-pi, pi) domain.
  
  The von Mises distribution is a special case of von Mises-Fisher distribution
  for n=2. However, the TFP's VonMisesFisher implementation represents the
  samples and location as (x, y) points on a circle, while VonMises represents
  them as scalar angles.
  
  #### Mathematical details
  
  The probability density function (pdf) of this distribution is,
  
  ```none
  pdf(x; loc, concentration) = exp(concentration cos(x - loc)) / Z
  Z = 2 * pi * I_0 (concentration)
  ```
  
  where:
  * `I_0 (concentration)` is the modified Bessel function of order zero;
  * `loc` the circular mean of the distribution, a scalar. It can take arbitrary
    values, but it is 2pi-periodic: loc and loc + 2pi result in the same
    distribution.
  * `concentration >= 0` parameter is the concentration parameter. When
  `concentration = 0`,
    this distribution becomes a Uniform distribution on [-pi, pi).
  
  #### Examples
  
  Examples of initialization of one or a batch of distributions.
  
  ```python
  # Create a batch of three von Mises distributions.
  loc = [1, 2, 3]
  concentration = [1, 2, 3]
  dist = tfp.distributions.VonMises(loc=[1.0, 2.0], concentration=[0.5, 2.0])
  
  dist.sample([3])  # Shape [3, 2]
  ```
  
  Arguments are broadcast when possible.
  
  ```python
  dist = tfp.distributions.VonMises(loc=1.0, concentration=[0.5, 2.0])
  
  # Evaluate the pdf of both distributions on the same point, 3.0,
  # returning a length 2 tensor.
  dist.prob(3.0)
  ```
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code: def _variance(self):
      concentration = tf.convert_to_tensor(self.concentration)
      concentration = tf.broadcast_to(
          concentration,
          self._batch_shape_tensor(concentration=concentration),
      )
      return 1.0 - tf.math.bessel_i1e(
          concentration
      ) / tf.math.bessel_i0e(concentration)
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import custom_gradient
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.math.gradient import value_and_gradient
label_desc: Use image node_id 14 for calling the VonMises obj's underlying member method code with example usage: obj._variance() and returns: unknown