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 sample_rep(
      self, num_samples: Optional[int] = None, dtype=np.float32
  ) -> Tensor:
      def s(mu: Tensor, sigma: Tensor) -> Tensor:
          raw_samples = self.F.sample_normal(
              mu=mu.zeros_like(), sigma=sigma.ones_like(), dtype=dtype
          )
          return sigma * raw_samples + mu
  
      return _sample_multiple(
          s, mu=self.mu, sigma=self.sigma, num_samples=num_samples
      )
  
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 12 for calling the Gaussian obj's underlying member method code with example usage: obj.sample_rep(num_samples, dtype) and returns: _sample_multiple, unknown