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 sample_rep(self, num_samples=None, dtype=np.float32) -> Tensor:
      F = self.F
  
      def s(mu: Tensor, b: Tensor) -> Tensor:
          ones = mu.ones_like()
          x = F.random.uniform(-0.5 * ones, 0.5 * ones, dtype=dtype)
          laplace_samples = mu - b * F.sign(x) * F.log(
              (1.0 - 2.0 * F.abs(x)).clip(1.0e-30, 1.0e30)
              # 1.0 - 2.0 * F.abs(x)
          )
          return laplace_samples
  
      return _sample_multiple(
          s, mu=self.mu, b=self.b, num_samples=num_samples
      )
  
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 10 for calling the Laplace obj's underlying member method code with example usage: obj.sample_rep(num_samples, dtype) and returns: _sample_multiple, laplace_samples