class_docstr: Laplace distribution.
  
  Parameters
  ----------
  mu
      Tensor containing the means, of shape `(*batch_shape, *event_shape)`.
  b
      Tensor containing the distribution scale, of shape
      `(*batch_shape, *event_shape)`.
  F
class_docstr_tok: ["Laplace","distribution",".","Parameters","--","--","--","--","--","mu","Tensor","containing","the","means",",","of","shape","`","(","*","batch_shape",",","*","event_shape",")","`",".","b","Tensor","containing","the","distribution","scale",",","of","shape","`","(","*","batch_shape",",","*","event_shape",")","`",".","F"]
code: def event_shape(self) -> Tuple:
      return ()
  
imports: from typing import Dict, List, Tuple
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .distribution import Distribution, _sample_multiple, getF, softplus
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 4 for calling the Laplace obj's underlying member method code with example usage: obj.event_shape() and returns: 