class_docstr: Laplace with softplus applied to `scale`.
code: def __init__(
      self,
      loc,
      scale,
      validate_args=False,
      allow_nan_stats=True,
      name="LaplaceWithSoftplusScale",
  ):
      parameters = dict(locals())
      with ops.name_scope(name, values=[loc, scale]) as name:
          super(LaplaceWithSoftplusScale, self).__init__(
              loc=loc,
              scale=nn.softplus(scale, name="softplus_scale"),
              validate_args=validate_args,
              allow_nan_stats=allow_nan_stats,
              name=name,
          )
      self._parameters = parameters
  
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 LaplaceWithSoftplusScale object from inherited base classes: Laplace with example: obj = LaplaceWithSoftplusScale(loc, scale, validate_args, allow_nan_stats, name)