docstr: Log interpretation of network predictions in tensorboard.
docstr_tok: ["Log","interpretation","of","network","predictions","in","tensorboard","."]
code: def log_interpretation(self, x, out, batch_idx):
      """
      Log interpretation of network predictions in tensorboard.
      """
      label = ["val", "train"][self.training]
      if self.log_interval > 0 and batch_idx % self.log_interval == 0:
          fig = self.plot_interpretation(x, out, idx=0)
          name = f"{label.capitalize()} interpretation of item 0 in "
          if self.training:
              name += f"step {self.global_step}"
          else:
              name += f"batch {batch_idx}"
          self.logger.experiment.add_figure(
              name, fig, global_step=self.global_step
          )
          if isinstance(fig, (list, tuple)):
              for idx, f in enumerate(fig):
                  self.logger.experiment.add_figure(
                      f"{self.target_names[idx]} {name}",
                      f,
                      global_step=self.global_step,
                  )
              else:
                  self.logger.experiment.add_figure(
                      name,
                      fig,
                      global_step=self.global_step,
                  )
  
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 10 for calling the NHiTS obj's underlying member method code with example usage: obj.log_interpretation(x, out, batch_idx) without return types