class_docstr: NegativeBinomial distribution.
  
  The NegativeBinomial distribution is related to the experiment of performing
  Bernoulli trials in sequence. Given a Bernoulli trial with probability `p` of
  success, the NegativeBinomial distribution represents the distribution over
  the number of successes `s` that occur until we observe `f` failures.
  
  The probability mass function (pmf) is,
  
  ```none
  pmf(s; f, p) = p**s (1 - p)**f / Z
  Z = s! (f - 1)! / (s + f - 1)!
  ```
  
  where:
  * `total_count = f`,
  * `probs = p`,
  * `Z` is the normalizaing constant, and,
  * `n!` is the factorial of `n`.
code: def _log_prob(self, x):
      total_count = tf.convert_to_tensor(self.total_count)
      logits = self._logits_parameter_no_checks()
      log_unnormalized_prob = total_count * tf.math.log_sigmoid(
          -logits
      ) + tf.math.multiply_no_nan(tf.math.log_sigmoid(logits), x)
      log_normalization = special.lbeta(
          1.0 + x, total_count
      ) + tf.math.log(total_count + x)
      return log_unnormalized_prob - log_normalization
  
imports: 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.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 reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math import special
  from tensorflow_probability.python.util.deferred_tensor import DeferredTensor
label_desc: Use image node_id 11 for calling the NegativeBinomial obj's underlying member method code with example usage: obj._log_prob(x) and returns: unknown