class_docstr: Exponential with softplus transform on `rate`.
class_docstr_tok: ["Exponential","with","softplus","transform","on","`","rate","`","."]
code: def __init__(
      self,
      rate,
      validate_args=False,
      allow_nan_stats=True,
      name="ExponentialWithSoftplusRate",
  ):
      parameters = dict(locals())
      with ops.name_scope(name, values=[rate]) as name:
          super(ExponentialWithSoftplusRate, self).__init__(
              rate=nn.softplus(rate, name="softplus_rate"),
              validate_args=validate_args,
              allow_nan_stats=allow_nan_stats,
              name=name,
          )
      self._parameters = parameters
  
file_path: tensorflow-upstream/tensorflow/python/ops/distributions/exponential.py
imports: import numpy
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_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 gamma
  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 ExponentialWithSoftplusRate object from inherited base classes: Exponential with example: obj = ExponentialWithSoftplusRate(rate, validate_args, allow_nan_stats, name)