code: def create_log(self, x, y, out, batch_idx):
      n_samples = [
          self.hparams.n_validation_samples,
          self.hparams.n_plotting_samples,
      ][self.training]
      log = super().create_log(
          x,
          y,
          out,
          batch_idx,
          prediction_kwargs=dict(n_samples=n_samples),
          quantiles_kwargs=dict(n_samples=n_samples),
      )
      return log
  
imports: from copy import copy, deepcopy
  from typing import Any, Dict, List, Literal, Optional, Tuple, Union
  import numpy
  import pandas
  import torch
  import torch.nn
  from torch.utils.data.dataloader import DataLoader
  from pytorch_forecasting.data.encoders import EncoderNormalizer, MultiNormalizer, NaNLabelEncoder
  from pytorch_forecasting.data.timeseries import TimeSeriesDataSet
  from pytorch_forecasting.metrics import MAE, MAPE, MASE, RMSE, SMAPE, DistributionLoss, Metric, MultiLoss, MultivariateDistributionLoss, NormalDistributionLoss
  from pytorch_forecasting.models.base_model import AutoRegressiveBaseModelWithCovariates, Prediction
  from pytorch_forecasting.models.nn import HiddenState, MultiEmbedding, get_rnn
  from pytorch_forecasting.utils import apply_to_list, to_list
label_desc: Use image node_id 8 for calling the DeepAR obj's underlying member method code with example usage: obj.create_log(x, y, out, batch_idx) and returns: log