class_docstr: The Laplace distribution with location `loc` and `scale` parameters.
  
  #### Mathematical details
  
  The probability density function (pdf) of this distribution is,
  
  ```none
  pdf(x; mu, sigma) = exp(-|x - mu| / sigma) / Z
  Z = 2 sigma
  ```
  
  where `loc = mu`, `scale = sigma`, and `Z` is the normalization constant.
  
  Note that the Laplace distribution can be thought of two exponential
  distributions spliced together "back-to-back."
  
  The Lpalce 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 ~ Laplace(loc=0, scale=1)
  Y = loc + scale * X
  ```
code: def _sample_n(self, n, seed=None):
      shape = array_ops.concat([[n], self.batch_shape_tensor()], 0)
      # Uniform variates must be sampled from the open-interval `(-1, 1)` rather
      # than `[-1, 1)`. In the case of `(0, 1)` we'd use
      # `np.finfo(self.dtype.as_numpy_dtype).tiny` because it is the smallest,
      # positive, "normal" number. However, the concept of subnormality exists
      # only at zero; here we need the smallest usable number larger than -1,
      # i.e., `-1 + eps/2`.
      uniform_samples = random_ops.random_uniform(
          shape=shape,
          minval=np.nextafter(
              self.dtype.as_numpy_dtype(-1.0),
              self.dtype.as_numpy_dtype(0.0),
          ),
          maxval=1.0,
          dtype=self.dtype,
          seed=seed,
      )
      return self.loc - self.scale * math_ops.sign(
          uniform_samples
      ) * math_ops.log1p(-math_ops.abs(uniform_samples))
  
imports: import math
  import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import check_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 distribution
  from tensorflow.python.ops.distributions import special_math
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 9 for calling the Laplace obj's underlying member method code with example usage: obj._sample_n(n, seed) and returns: unknown