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 cdf(self, x: Tensor) -> Tensor:
      y = (x - self.mu) / self.b
      return 0.5 + 0.5 * y.sign() * (1.0 - self.F.exp(-y.abs()))
  
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 9 for calling the Laplace obj's underlying member method code with example usage: obj.cdf(x) and returns: unknown