class_docstr: Create a laplace distribution object.
  
  Parameters
  ----------
  loc : Tensor or scalar, default 0
      mean of the distribution.
  scale : Tensor or scalar, default 1
      scale of the distribution
class_docstr_tok: ["Create","a","laplace","distribution","object",".","Parameters","--","--","--","--","--","loc",":","Tensor","or","scalar",",","default","0","mean","of","the","distribution",".","scale",":","Tensor","or","scalar",",","default","1","scale","of","the","distribution"]
code: def broadcast_to(self, batch_shape):
      new_instance = self.__new__(type(self))
      new_instance.loc = np.broadcast_to(self.loc, batch_shape)
      new_instance.scale = np.broadcast_to(self.scale, batch_shape)
      super(Laplace, 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/laplace.py
imports: from .constraint import Real, Positive
  from .distribution import Distribution
  from .utils import sample_n_shape_converter
  from ....None import np
label_desc: Use image node_id 5 for calling the Laplace obj's underlying member method code with example usage: obj.broadcast_to(batch_shape) and returns: new_instance