class_docstr: `Gamma` with softplus of `concentration` and `rate`.
code: def __init__(
      self,
      concentration,
      rate,
      validate_args=False,
      allow_nan_stats=True,
      name="GammaWithSoftplusConcentrationRate",
  ):
      parameters = dict(locals())
      with ops.name_scope(name, values=[concentration, rate]) as name:
          super(GammaWithSoftplusConcentrationRate, self).__init__(
              concentration=nn.softplus(
                  concentration, name="softplus_concentration"
              ),
              rate=nn.softplus(rate, name="softplus_rate"),
              validate_args=validate_args,
              allow_nan_stats=allow_nan_stats,
              name=name,
          )
      self._parameters = parameters
  
imports: 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 control_flow_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 kullback_leibler
  from tensorflow.python.ops.distributions import util
  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 GammaWithSoftplusConcentrationRate object from inherited base classes: Gamma with example: obj = GammaWithSoftplusConcentrationRate(concentration, rate, validate_args, allow_nan_stats, name)