class_docstr: Bijector mapping R**n to non-negative reals.
  
  Forward computation maps R**n to the support of the `GeneralizedPareto`
  distribution with parameters `loc`, `scale`, and `concentration`.
  
  #### Mathematical Details
  
  The forward computation from `y` in R**n to `x` constrains `x` as follows:
  
  `x >= loc`                                             if `concentration >= 0`
  `x >= loc` and `x <= loc + scale / abs(concentration)` if `concentration < 0`
  
  This bijector is used as the `experimental_default_event_space_bijector` of
  the `GeneralizedPareto` distribution.
class_docstr_tok: ["Bijector","mapping","R","*","*","n","to","non-negative","reals",".","Forward","computation","maps","R","*","*","n","to","the","support","of","the","`","GeneralizedPareto","`","distribution","with","parameters","`","loc","`",",","`","scale","`",",","and","`","concentration","`",".","#","#","#","#","Mathematical","Details","The","forward","computation","from","`","y","`","in","R","*","*","n","to","`","x","`","constrains","`","x","`","as","follows",":","`","x",">","=","loc","`","if","`","concentration",">","=","0","`","`","x",">","=","loc","`","and","`","x","<","=","loc","+","scale","\/","abs","(","concentration",")","`","if","`","concentration","<","0","`","This","bijector","is","used","as","the","`","experimental_default_event_space_bijector","`","of","the","`","GeneralizedPareto","`","distribution","."]
code: def __init__(
      self,
      loc,
      scale,
      concentration,
      validate_args=False,
      name="generalized_pareto",
  ):
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          dtype = dtype_util.common_dtype(
              [loc, scale, concentration], dtype_hint=tf.float32
          )
  
          self._loc = tensor_util.convert_nonref_to_tensor(loc)
          self._scale = tensor_util.convert_nonref_to_tensor(scale)
          self._concentration = tensor_util.convert_nonref_to_tensor(
              concentration
          )
          self._non_negative_concentration_bijector = (
              chain_bijector.Chain(
                  [
                      shift_bijector.Shift(
                          shift=self._loc, validate_args=validate_args
                      ),
                      softplus_bijector.Softplus(
                          validate_args=validate_args
                      ),
                  ],
                  validate_args=validate_args,
              )
          )
          super(GeneralizedPareto, self).__init__(
              validate_args=validate_args,
              forward_min_event_ndims=0,
              dtype=dtype,
              parameters=parameters,
              name=name,
          )
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import tensor_util
label_desc: Use image node_id 1 to create a new GeneralizedPareto object from inherited base classes: bijector_lib, bijector_lib with example: obj = GeneralizedPareto(loc, scale, concentration, validate_args, name)