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
  ```
docstr: Construct Laplace distribution with parameters `loc` and `scale`.
  
  The parameters `loc` and `scale` must be shaped in a way that supports
  broadcasting (e.g., `loc / scale` is a valid operation).
  
  Args:
    loc: Floating point tensor which characterizes the location (center)
      of the distribution.
    scale: Positive floating point tensor which characterizes the spread of
      the distribution.
    validate_args: Python `bool`, default `False`. When `True` distribution
      parameters are checked for validity despite possibly degrading runtime
      performance. When `False` invalid inputs may silently render incorrect
      outputs.
    allow_nan_stats: Python `bool`, default `True`. When `True`,
      statistics (e.g., mean, mode, variance) use the value '`NaN`' to
      indicate the result is undefined. When `False`, an exception is raised
      if one or more of the statistic's batch members are undefined.
    name: Python `str` name prefixed to Ops created by this class.
  
  Raises:
    TypeError: if `loc` and `scale` are of different dtype.
code: def __init__(
      self,
      loc,
      scale,
      validate_args=False,
      allow_nan_stats=True,
      name="Laplace",
  ):
      """Construct Laplace distribution with parameters `loc` and `scale`.
  
      The parameters `loc` and `scale` must be shaped in a way that supports
      broadcasting (e.g., `loc / scale` is a valid operation).
  
      Args:
        loc: Floating point tensor which characterizes the location (center)
          of the distribution.
        scale: Positive floating point tensor which characterizes the spread of
          the distribution.
        validate_args: Python `bool`, default `False`. When `True` distribution
          parameters are checked for validity despite possibly degrading runtime
          performance. When `False` invalid inputs may silently render incorrect
          outputs.
        allow_nan_stats: Python `bool`, default `True`. When `True`,
          statistics (e.g., mean, mode, variance) use the value '`NaN`' to
          indicate the result is undefined. When `False`, an exception is raised
          if one or more of the statistic's batch members are undefined.
        name: Python `str` name prefixed to Ops created by this class.
  
      Raises:
        TypeError: if `loc` and `scale` are of different dtype.
      """
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          dtype = dtype_util.common_dtype([loc, scale], tf.float32)
          self._loc = tensor_util.convert_nonref_to_tensor(
              loc, name="loc", dtype=dtype
          )
          self._scale = tensor_util.convert_nonref_to_tensor(
              scale, name="scale", dtype=dtype
          )
          dtype_util.assert_same_float_dtype([self._loc, self._scale])
          super(Laplace, self).__init__(
              dtype=dtype,
              reparameterization_type=reparameterization.FULLY_REPARAMETERIZED,
              validate_args=validate_args,
              allow_nan_stats=allow_nan_stats,
              parameters=parameters,
              name=name,
          )
  
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 1 to create a new Laplace object from inherited base classes: distribution with example: obj = Laplace(loc, scale, validate_args, allow_nan_stats, name)