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
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code: def __init__(self, mu: Tensor, sigma: Tensor) -> None:
      self.mu = mu
      self.sigma = 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 1 to create a new Gaussian object from inherited base classes: Distribution with example: obj = Gaussian(mu, sigma)