class_docstr: ProbitBernoulli distribution.
  
  The ProbitBernoulli distribution with `probs` parameter, i.e., the probability
  of a `1` outcome (vs a `0` outcome). Unlike a regular Bernoulli distribution,
  which uses the logistic (aka 'sigmoid') function to go from the un-constrained
  parameters to probabilities, this distribution uses the CDF of the [standard
  normal distribution](https://en.wikipedia.org/wiki/Normal_distribution):
  
  ```none
  p(x=1; probits) = 0.5 * (1 + erf(probits / sqrt(2)))
  p(x=0; probits) = 1 - p(x=1; probits)
  ```
  
  Where `erf` is the [error
  function](https://en.wikipedia.org/wiki/Error_function). A typical application
  of this distribution is in [probit
  regression](https://en.wikipedia.org/wiki/Probit_model).
code: def _event_shape_tensor(self):
      return tf.constant([], dtype=tf.int32)
  
imports: 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 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 special_math
  from tensorflow_probability.python.internal import tensor_util
label_desc: Use image node_id 5 for calling the ProbitBernoulli obj's underlying member method code with example usage: obj._event_shape_tensor() and returns: tf