class_docstr: A mixture of a point-mass and another distribution.
  
  Under the hood, this is implemented as a mixture.Mixture, and so
  supports all of the methods of that class.
  
  ### Examples:
  
  ```python
  zinb = Inflated(
           tfd.NegativeBinomial(5.0, probs=0.1), inflated_loc_prob=0.2)
  sample = zinb.sample(seed=jax.random.PRNGKey(0))
  ```
code: def _log_prob(self, x):
      # We override the log_prob implementation from Mixture in the case
      # where we are inflating a continuous distribution, because we have
      # found that this "censored" version gives a good maximum likelihood
      # estimate of the continuous distribution's parameters but the
      # default implementation doesn't.  This follows the proposal in
      # https://arxiv.org/pdf/2010.09647.pdf for summing distributions of
      # different Hausdorff dimension.
      if isinstance(
          self._distribution, distribution_lib.DiscreteDistributionMixin
      ):
          return super(_Inflated, self)._log_prob(x)
      else:
          # Enable non-NaN gradients of the log_prob, even if the gradient of
          # the continuous distribution is NaN at _inflated_loc.  See
          # https://github.com/tensorflow/probability/blob/main/discussion/where-nan.pdf
          # for details.
          safe_x = tf.where(
              self._almost_inflated_loc(x),
              _safe_value_for_distribution(self._distribution),
              x,
          )
          return tf.where(
              self._almost_inflated_loc(x),
              self._categorical_dist.log_prob(0),
              self._categorical_dist.log_prob(1)
              + self._distribution.log_prob(safe_x),
          )
  
imports: import inspect
  import warnings
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import deterministic
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import mixture
  from tensorflow_probability.python.distributions import negative_binomial
  from tensorflow_probability.python.internal import auto_composite_tensor
  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 samplers
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.util.deferred_tensor import DeferredTensor
label_desc: Use image node_id 4 for calling the _Inflated obj's underlying member method code with example usage: obj._log_prob(x) and returns: super, tf