class_docstr: Create a Exponential distribution object parameterized by `scale`.
  
  Parameters
  ----------
  scale : Tensor or scalar
     Scale of the distribution. (scale = 1 /rate)
class_docstr_tok: ["Create","a","Exponential","distribution","object","parameterized","by","`","scale","`",".","Parameters","--","--","--","--","--","scale",":","Tensor","or","scalar","Scale","of","the","distribution",".","(","scale","=","1","\/rate",")"]
code: def __init__(self, scale=1.0, validate_args=None):
      self.scale = scale
      super(Exponential, self).__init__(
          event_dim=0, validate_args=validate_args
      )
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/exponential.py
imports: from .exp_family import ExponentialFamily
  from .constraint import Positive
  from .utils import sample_n_shape_converter, cached_property
  from ....None import np
label_desc: Use image node_id 1 to create a new Exponential object from inherited base classes: ExponentialFamily with example: obj = Exponential(scale, validate_args)