class_docstr: Implicit Quantile Network Distribution Loss.
  
  Based on `Probabilistic Time Series Forecasting with Implicit Quantile Networks
  <https://arxiv.org/pdf/2107.03743.pdf>`_.
  A network is used to directly map network outputs to a quantile.
class_docstr_tok: ["Implicit","Quantile","Network","Distribution","Loss",".","Based","on","`","Probabilistic","Time","Series","Forecasting","with","Implicit","Quantile","Networks","<","https",":","\/\/arxiv.org\/pdf\/2107.03743.pdf",">","`","_",".","A","network","is","used","to","directly","map","network","outputs","to","a","quantile","."]
code: def rescale_parameters(
      self,
      parameters: torch.Tensor,
      target_scale: torch.Tensor,
      encoder: BaseEstimator,
  ) -> torch.Tensor:
      self._transformation = encoder.transformation
      return torch.concat(
          [
              parameters,
              target_scale.unsqueeze(1).expand(
                  -1, parameters.size(1), -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 4 for calling the ImplicitQuantileNetworkDistributionLoss obj's underlying member method code with example usage: obj.rescale_parameters(parameters, target_scale, encoder) and returns: torch