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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docstr: Returns an instance of `Gaussian` after fitting parameters to the given data.
  
  Parameters
  ----------
  F
  samples
      Tensor of shape (num_samples, batch_size, seq_len)
  
  Returns
  -------
  Distribution instance of type `Gaussian`.
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code: def fit(cls, F, samples: Tensor):
      """
      Returns an instance of `Gaussian` after fitting parameters to the given data.
  
      Parameters
      ----------
      F
      samples
          Tensor of shape (num_samples, batch_size, seq_len)
  
      Returns
      -------
      Distribution instance of type `Gaussian`.
  
      """
  
      # Compute mean and standard deviations
      mu = samples.mean(axis=0)
      sigma = F.sqrt(F.square(samples - mu).mean(axis=0))
      return cls(mu=mu, 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 9 for calling the Gaussian obj's underlying member method code with example usage: obj.fit(cls, F, samples) and returns: cls