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 sample(self, num_samples=None, dtype=np.float32):
      def s(mu):
          F = self.F
          q_min = 1e-3
          q_max = 1 - q_min
          sample = F.sample_uniform(
              F.ones_like(mu) * F.full(1, q_min),
              F.ones_like(mu) * F.full(1, q_max),
          )
          transf_sample = self.quantile(sample)
          return transf_sample
  
      mult_samp = _sample_multiple(s, self.mu, num_samples=num_samples)
      return mult_samp
  
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 7 for calling the LogitNormal obj's underlying member method code with example usage: obj.sample(num_samples, dtype) and returns: mult_samp, transf_sample