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 Laplace 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):
      loc = tf.convert_to_tensor(self.loc)
      scale = tf.convert_to_tensor(self.scale)
      shape = ps.concat(
          [[n], self._batch_shape_tensor(loc=loc, scale=scale)], 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(dtype_util.as_numpy_dtype(self.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`.
      dt = dtype_util.as_numpy_dtype(self.dtype)
      uniform_samples = samplers.uniform(
          shape=shape,
          minval=np.nextafter(dt(-1.0), dt(1.0)),
          maxval=1.0,
          dtype=self.dtype,
          seed=seed,
      )
      return loc - scale * tf.sign(uniform_samples) * tf.math.log1p(
          -tf.abs(uniform_samples)
      )
  
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
  from tensorflow_probability.python.stats import quantiles
label_desc: Use image node_id 7 for calling the Laplace obj's underlying member method code with example usage: obj._sample_n(n, seed) and returns: unknown