docstr: Computes log-pdf of a log-gamma random variable.
  
  Args:
    x: Value of the random variable.
    event_dims: Dimensions not to treat as independent.
  
  Returns:
    log_prob: The log-pdf up to a normalizing constant.
docstr_tok: ["Computes","log-pdf","of","a","log-gamma","random","variable",".","Args",":","x",":","Value","of","the","random","variable",".","event_dims",":","Dimensions","not","to","treat","as","independent",".","Returns",":","log_prob",":","The","log-pdf","up","to","a","normalizing","constant","."]
code: def _log_gamma_log_prob(self, x, event_dims=()):
      """Computes log-pdf of a log-gamma random variable.
  
      Args:
        x: Value of the random variable.
        event_dims: Dimensions not to treat as independent.
  
      Returns:
        log_prob: The log-pdf up to a normalizing constant.
      """
      return tf.reduce_sum(
          self._shape_param * x - self._rate_param * tf.exp(x),
          axis=event_dims,
      )
  
file_path: probability/tensorflow_probability/python/mcmc/internal/leapfrog_integrator_test.py
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc.internal import leapfrog_integrator
label_desc: Use image node_id 3 for calling the LeapfrogIntegratorTest obj's underlying member method code with example usage: obj._log_gamma_log_prob(x, event_dims) and returns: tf