class_docstr: Gaussian distribution.
  
  Parameters
  ----------
  mu
      Tensor containing the means, of shape `(*batch_shape, *event_shape)`.
  std
      Tensor containing the standard deviations, of shape
      `(*batch_shape, *event_shape)`.
  F
class_docstr_tok: ["Gaussian","distribution",".","Parameters","--","--","--","--","--","mu","Tensor","containing","the","means",",","of","shape","`","(","*","batch_shape",",","*","event_shape",")","`",".","std","Tensor","containing","the","standard","deviations",",","of","shape","`","(","*","batch_shape",",","*","event_shape",")","`",".","F"]
code: def log_prob(self, x: Tensor) -> Tensor:
      F = self.F
      mu, sigma = self.mu, self.sigma
      return -1.0 * (
          F.log(sigma)
          + 0.5 * math.log(2 * math.pi)
          + 0.5 * F.square((x - mu) / sigma)
      )
  
imports: import math
  from functools import partial
  from typing import Dict, List, Optional, 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 Gaussian obj's underlying member method code with example usage: obj.log_prob(x) and returns: unknown