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 quantile(self, level: Tensor) -> Tensor:
      F = self.F
      for _ in range(self.all_dim):
          level = level.expand_dims(axis=-1)
  
      condition = F.broadcast_greater(level, level.zeros_like() + 0.5)
      u = F.where(
          condition, F.log(2.0 * level), -F.log(2.0 - 2.0 * level)
      )
  
      return F.broadcast_add(self.mu, F.broadcast_mul(self.b, u))
  
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 11 for calling the Laplace obj's underlying member method code with example usage: obj.quantile(level) and returns: F