class_docstr: The logit-normal distribution.
  
  Parameters
  ----------
  mu
      Tensor containing the location, of shape
      `(*batch_shape, *event_shape)`.
  sigma
      Tensor indicating the scale, of shape `(*batch_shape, *event_shape)`.
  F
code: def quantile(self, level: Tensor) -> Tensor:
      F = self.F
      exp = F.exp(
          self.mu
          + (
              self.sigma
              * F.sqrt(F.full(1, 2))
              * F.erfinv(2 * level - 1)
          )
      )
      return exp / (1 + exp)
  
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 8 for calling the LogitNormal obj's underlying member method code with example usage: obj.quantile(level) and returns: unknown