class_docstr: The Normal distribution with location `loc` and `scale` parameters.
  
  #### Mathematical details
  
  The probability density function (pdf) is,
  
  ```none
  pdf(x; mu, sigma) = exp(-0.5 (x - mu)**2 / sigma**2) / Z
  Z = (2 pi sigma**2)**0.5
  ```
  
  where `loc = mu` is the mean, `scale = sigma` is the std. deviation, and, `Z`
  is the normalization constant.
  
  The Normal distribution is a member of the [location-scale family](
  https://en.wikipedia.org/wiki/Location-scale_family), i.e., it can be
  constructed as,
  
  ```none
  X ~ Normal(loc=0, scale=1)
  Y = loc + scale * X
  ```
  
  #### Examples
  
  Examples of initialization of one or a batch of distributions.
  
  ```python
  import tensorflow_probability as tfp
  tfd = tfp.distributions
  
  # Define a single scalar Normal distribution.
  dist = tfd.Normal(loc=0., scale=3.)
  
  # Evaluate the cdf at 1, returning a scalar.
  dist.cdf(1.)
  
  # Define a batch of two scalar valued Normals.
  # The first has mean 1 and standard deviation 11, the second 2 and 22.
  dist = tfd.Normal(loc=[1, 2.], scale=[11, 22.])
  
  # Evaluate the pdf of the first distribution on 0, and the second on 1.5,
  # returning a length two tensor.
  dist.prob([0, 1.5])
  
  # Get 3 samples, returning a 3 x 2 tensor.
  dist.sample([3])
  ```
  
  Arguments are broadcast when possible.
  
  ```python
  # Define a batch of two scalar valued Normals.
  # Both have mean 1, but different standard deviations.
  dist = tfd.Normal(loc=1., scale=[11, 22.])
  
  # Evaluate the pdf of both distributions on the same point, 3.0,
  # returning a length 2 tensor.
  dist.prob(3.0)
  ```
code: def _log_survival_function(self, x):
      return special_math.log_ndtr(-self._z(x))
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import special_math
  from tensorflow_probability.python.internal import tensor_util
label_desc: Use image node_id 11 for calling the Normal obj's underlying member method code with example usage: obj._log_survival_function(x) and returns: special_math