class_docstr: The log-logistic distribution.
class_docstr_tok: ["The","log-logistic","distribution","."]
code: def _log_prob(self, x):
      scale = tf.convert_to_tensor(self.scale)
      loc = tf.convert_to_tensor(self.loc)
      log_z = self._log_z(x, loc=loc, scale=scale)
      answer = (
          -tf.math.log(scale)
          - loc
          + (1.0 - scale) * log_z
          - 2 * tf.math.softplus(log_z)
      )
      # The formula computes `nan` for `x == +inf`.  However, it shouldn't be too
      # inaccurate for large finite `x`, because `x` only appears as `log(x)`, and
      # `log` is effectively discountinuous at `+inf`.
      return tf.where(
          x >= np.inf, tf.constant(-np.inf, dtype=answer.dtype), answer
      )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
label_desc: Use image node_id 10 for calling the LogLogistic obj's underlying member method code with example usage: obj._log_prob(x) and returns: tf