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 log_prob(self, x: Tensor) -> Tensor:
      F = self.F
      return -1.0 * (
          F.log(2.0 * self.b) + F.abs((x - self.mu) / self.b)
      )
  
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 6 for calling the Laplace obj's underlying member method code with example usage: obj.log_prob(x) and returns: unknown