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 log_prob(self, x: Tensor) -> Tensor:
      F = self.F
      x_clip = 1e-3
      x = F.clip(x, x_clip, 1 - x_clip)
      log_prob = -1.0 * (
          F.log(self.sigma)
          + F.log(F.sqrt(2 * F.full(1, np.pi)))
          + F.log(x)
          + F.log(1 - x)
          + (
              (F.log(x) - F.log(1 - x) - self.mu) ** 2
              / (2 * (self.sigma**2))
          )
      )
      return log_prob
  
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 LogitNormal obj's underlying member method code with example usage: obj.log_prob(x) and returns: log_prob