code: def _kl_exponential_exponential(p, q):
      scale_ratio = p.scale / q.scale
      t1 = -np.log(scale_ratio)
      return t1 + scale_ratio - 1
  
imports: import math
  import numpy
  from ....None import np
  from .utils import gammaln, digamma
  from .exponential import Exponential
  from .pareto import Pareto
  from .uniform import Uniform
  from .normal import Normal
  from .laplace import Laplace
  from .cauchy import Cauchy
  from .poisson import Poisson
  from .geometric import Geometric
  from .gamma import Gamma
  from .dirichlet import Dirichlet
  from .beta import Beta
  from .half_normal import HalfNormal
  from .bernoulli import Bernoulli
  from .binomial import Binomial
  from .gumbel import Gumbel
  from .categorical import Categorical
  from .one_hot_categorical import OneHotCategorical
  from .multivariate_normal import MultivariateNormal
label_desc: Use image node_id 13 for calling a global function with example usage: _kl_exponential_exponential(p, q) and returns: unknown