class_docstr: Continuous Bernoulli distribution.
  
  This distribution is parameterized by `probs`, a (batch of) parameters
  taking values in `(0, 1)`. Note that, unlike in the Bernoulli case, `probs`
  does not correspond to a probability, but the same name is used due to the
  similarity with the Bernoulli.
  
  #### Mathematical Details
  
  The continuous Bernoulli is a distribution over the interval `[0, 1]`,
  parameterized by `probs` in `(0, 1)`.
  
  The probability density function (pdf) is,
  
  ```none
  pdf(x; probs) = probs**x * (1 - probs)**(1 - x) * C(probs)
  C(probs) = (2 * atanh(1 - 2 * probs) / (1 - 2 * probs) if probs != 0.5
              else 2.)
  ```
  
  While the normalizing constant `C(probs)` is a continuous function of `probs`
  (even at `probs = 0.5`), computing it at values close to 0.5 can result in
  numerical instabilities due to 0/0 errors. A Taylor approximation of
  `C(probs)` is thus used for values of `probs`
  in a small interval around 0.5.
  
  NOTE: Unlike the Bernoulli, numerical instabilities can happen for `probs`
  very close to 0 or 1. Current implementation allows any value in `(0, 1)`,
  but this could be changed to `(1e-6, 1-1e-6)` to avoid these issues.
  
  #### References
  
  [1] Loaiza-Ganem G and Cunningham JP. The continuous Bernoulli: fixing a
      pervasive error in variational autoencoders. NeurIPS2019.
      https://arxiv.org/abs/1907.06845
code: def _parameter_control_dependencies(self, is_init):
      return maybe_assert_continuous_bernoulli_param_correctness(
          is_init, self.validate_args, self._probs, self._logits
      )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import assert_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.math import generic
label_desc: Use image node_id 22 for calling the ContinuousBernoulli obj's underlying member method code with example usage: obj._parameter_control_dependencies(is_init) and returns: maybe_assert_continuous_bernoulli_param_correctness