class_docstr: Multivariate low-rank normal distribution loss.
  
  Use this loss to make out of a DeepAR model a DeepVAR network.
class_docstr_tok: ["Multivariate","low-rank","normal","distribution","loss",".","Use","this","loss","to","make","out","of","a","DeepAR","model","a","DeepVAR","network","."]
code: def rescale_parameters(
      self,
      parameters: torch.Tensor,
      target_scale: torch.Tensor,
      encoder: BaseEstimator,
  ) -> torch.Tensor:
      self._transformation = encoder.transformation
  
      # scale
      loc = parameters[..., 0].unsqueeze(-1)
      scale = (
          F.softplus(parameters[..., 1].unsqueeze(-1) + self._diag_bias)
          + self.sigma_minimum**2
      )
  
      cov_factor = parameters[..., 2:] / self._cov_factor_scale
      return torch.concat(
          [
              target_scale.unsqueeze(1).expand(-1, loc.size(1), -1),
              loc,
              scale,
              cov_factor,
          ],
          dim=-1,
      )
  
imports: from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  from sklearn.base import BaseEstimator
  import torch
  from torch import distributions, nn
  import torch.nn.functional
  from pytorch_forecasting.data.encoders import TorchNormalizer, softplus_inv
  from pytorch_forecasting.metrics.base_metrics import DistributionLoss, MultivariateDistributionLoss
label_desc: Use image node_id 3 for calling the MultivariateNormalDistributionLoss obj's underlying member method code with example usage: obj.rescale_parameters(parameters, target_scale, encoder) and returns: torch