class_docstr: The log-logistic distribution.
class_docstr_tok: ["The","log-logistic","distribution","."]
code: def _mean(self):
      scale = tf.convert_to_tensor(self.scale)
      with tf.control_dependencies(
          []
          if self.allow_nan_stats
          else [  # pylint: disable=g-long-ternary
              assert_util.assert_less(
                  scale,
                  tf.ones([], dtype=self.dtype),
                  message="Mean undefined for scale >= 1.",
              ),
          ]
      ):
          mean = tf.math.exp(self.loc) / sinc(scale)
          return tf.where(
              scale > 1.0, tf.cast(np.nan, dtype=self.dtype), mean
          )
  
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 5 for calling the LogLogistic obj's underlying member method code with example usage: obj._mean() and returns: tf