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
  ```
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.
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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 ops.name_scope(name, values=[loc, scale]) as name:
          with ops.control_dependencies(
              [check_ops.assert_positive(scale)]
              if validate_args
              else []
          ):
              self._loc = array_ops.identity(loc, name="loc")
              self._scale = array_ops.identity(scale, name="scale")
              check_ops.assert_same_float_dtype(
                  [self._loc, self._scale]
              )
          super(Laplace, self).__init__(
              dtype=self._loc.dtype,
              reparameterization_type=distribution.FULLY_REPARAMETERIZED,
              validate_args=validate_args,
              allow_nan_stats=allow_nan_stats,
              parameters=parameters,
              graph_parents=[self._loc, self._scale],
              name=name,
          )
  
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 1 to create a new Laplace object from inherited base classes: distribution with example: obj = Laplace(loc, scale, validate_args, allow_nan_stats, name)