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 broadcast_to(self, batch_shape):
      new_instance = self.__new__(type(self))
      new_instance.scale = np.broadcast_to(self.scale, batch_shape)
      super(Exponential, new_instance).__init__(
          event_dim=self.event_dim, validate_args=False
      )
      new_instance._validate_args = self._validate_args
      return new_instance
  
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 8 for calling the Exponential obj's underlying member method code with example usage: obj.broadcast_to(batch_shape) and returns: new_instance