class_docstr: Multivariate quantile loss based on the article
  `Multivariate Quantile Function Forecaster <http://arxiv.org/abs/2202.11316>`_.
  
  Requires install of additional library:
  ``pip install pytorch-forecasting[mqf2]``
class_docstr_tok: ["Multivariate","quantile","loss","based","on","the","article","`","Multivariate","Quantile","Function","Forecaster","<","http",":","\/\/arxiv.org\/abs\/2202.11316",">","`","_",".","Requires","install","of","additional","library",":","``","pip","install","pytorch-forecasting","[","mqf2","]","``"]
code: def rescale_parameters(
      self,
      parameters: torch.Tensor,
      target_scale: torch.Tensor,
      encoder: BaseEstimator,
  ) -> torch.Tensor:
      self._transformation = encoder.transformation
      return torch.concat(
          [parameters.reshape(parameters.size(0), -1), target_scale],
          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 5 for calling the MQF2DistributionLoss obj's underlying member method code with example usage: obj.rescale_parameters(parameters, target_scale, encoder) and returns: torch