docstr: Take training / validation step.
docstr_tok: ["Take","training","\/","validation","step","."]
code: def step(self, x, y, batch_idx) -> Dict[str, torch.Tensor]:
      """
      Take training / validation step.
      """
      log, out = super().step(x, y, batch_idx=batch_idx)
  
      if (
          self.hparams.backcast_loss_ratio > 0 and not self.predicting
      ):  # add loss from backcast
          backcast = out["backcast"]
          backcast_weight = (
              self.hparams.backcast_loss_ratio
              * self.hparams.prediction_length
              / self.hparams.context_length
          )
          backcast_weight = backcast_weight / (
              backcast_weight + 1
          )  # normalize
          forecast_weight = 1 - backcast_weight
          if isinstance(self.loss, (MultiLoss, MASE)):
              backcast_loss = (
                  self.loss(
                      backcast,
                      (x["encoder_target"], None),
                      encoder_target=x["decoder_target"],
                      encoder_lengths=x["decoder_lengths"],
                  )
                  * backcast_weight
              )
          else:
              backcast_loss = (
                  self.loss(backcast, x["encoder_target"])
                  * backcast_weight
              )
          label = ["val", "train"][self.training]
          self.log(
              f"{label}_backcast_loss",
              backcast_loss,
              on_epoch=True,
              on_step=self.training,
              batch_size=len(x["decoder_target"]),
          )
          self.log(
              f"{label}_forecast_loss",
              log["loss"],
              on_epoch=True,
              on_step=self.training,
              batch_size=len(x["decoder_target"]),
          )
          log["loss"] = log["loss"] * forecast_weight + backcast_loss
  
      # log interpretation
      self.log_interpretation(x, out, batch_idx=batch_idx)
      return log, out
  
imports: from copy import copy
  from typing import Dict, List, Optional, Tuple, Union
  from matplotlib import pyplot
  import numpy
  import torch
  from torch import nn
  from pytorch_forecasting.data import TimeSeriesDataSet
  from pytorch_forecasting.data.encoders import NaNLabelEncoder
  from pytorch_forecasting.metrics import MAE, MAPE, MASE, RMSE, SMAPE, MultiHorizonMetric, MultiLoss
  from pytorch_forecasting.models.base_model import BaseModelWithCovariates
  from pytorch_forecasting.models.nhits.sub_modules import NHiTS
  from pytorch_forecasting.models.nn.embeddings import MultiEmbedding
  from pytorch_forecasting.utils import create_mask, detach, to_list
label_desc: Use image node_id 8 for calling the NHiTS obj's underlying member method code with example usage: obj.step(x, y, batch_idx) and returns: log,  out