class_docstr: Exponential distribution.
  
  The Exponential distribution is parameterized by an event `rate` parameter.
  
  #### Mathematical Details
  
  The probability density function (pdf) is,
  
  ```none
  pdf(x; lambda, x > 0) = exp(-lambda x) / Z
  Z = 1 / lambda
  ```
  
  where `rate = lambda` and `Z` is the normalizaing constant.
  
  The Exponential distribution is a special case of the Gamma distribution,
  i.e.,
  
  ```python
  Exponential(rate) = Gamma(concentration=1., rate)
  ```
  
  The Exponential distribution uses a `rate` parameter, or "inverse scale",
  which can be intuited as,
  
  ```none
  X ~ Exponential(rate=1)
  Y = X / rate
  ```
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code: def _log_survival_function(self, value):
      return self._log_prob(value) - math_ops.log(self._rate)
  
file_path: tensorflow-upstream/tensorflow/python/ops/distributions/exponential.py
imports: import numpy
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops.distributions import gamma
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 4 for calling the Exponential obj's underlying member method code with example usage: obj._log_survival_function(value) and returns: unknown