class_docstr: Normal distribution loss.
class_docstr_tok: ["Normal","distribution","loss","."]
code: def rescale_parameters(
      self,
      parameters: torch.Tensor,
      target_scale: torch.Tensor,
      encoder: BaseEstimator,
  ) -> torch.Tensor:
      self._transformation = encoder.transformation
      loc = parameters[..., 0]
      scale = F.softplus(parameters[..., 1])
      return torch.concat(
          [
              target_scale.unsqueeze(1).expand(-1, loc.size(1), -1),
              loc.unsqueeze(-1),
              scale.unsqueeze(-1),
          ],
          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 2 for calling the NormalDistributionLoss obj's underlying member method code with example usage: obj.rescale_parameters(parameters, target_scale, encoder) and returns: torch