code: def __call__(
      self,
      ds: EncodedDs,
      args: PredictionArguments = PredictionArguments(),
  ) -> pd.DataFrame:
      original_df = deepcopy(ds.data_frame)
  
      self.model = self.model.eval()
      decoded_predictions = []
      all_probs: List[List[float]] = []
      rev_map = {}
  
      length = (
          sum(ds.encoded_ds_lengths)
          if isinstance(ds, ConcatedEncodedDs)
          else len(ds)
      )
      pred_cols = [
          f"prediction_{i}"
          for i in range(self.timeseries_settings.horizon)
      ]
      ydf = pd.DataFrame(
          0,  # zero-filled
          index=np.arange(length),
          dtype=object,
          columns=pred_cols,
      )
  
      with torch.no_grad():
          for idx, (X, Y) in enumerate(ds):
              X = X.to(self.model.device)
              Yh = self.model(X)
              Yh = torch.unsqueeze(Yh, 0) if len(Yh.shape) < 2 else Yh
  
              kwargs = {}
              for dep in self.target_encoder.dependencies:
                  kwargs["dependency_data"] = {
                      dep: ds.data_frame.iloc[idx][[dep]].values
                  }
  
              if args.predict_proba and self.supports_proba:
                  (
                      decoded_prediction,
                      probs,
                      rev_map,
                  ) = self.target_encoder.decode_probabilities(
                      Yh, **kwargs
                  )
                  all_probs.append(probs)
              else:
                  decoded_prediction = self.target_encoder.decode(
                      Yh, **kwargs
                  )
  
              decoded_predictions.extend(decoded_prediction)
  
      decoded_predictions = np.array(decoded_predictions)
      if len(decoded_predictions.shape) == 1:
          decoded_predictions = np.expand_dims(
              decoded_predictions, axis=1
          )
      ydf[pred_cols] = decoded_predictions
  
      ydf["prediction"] = ydf.values.tolist()
  
      if self.timeseries_settings.horizon == 1:
          ydf["prediction"] = [p[0] for p in ydf["prediction"]]
  
      if args.predict_proba and self.supports_proba:
          raw_predictions = np.array(all_probs).squeeze(axis=1)
  
          for idx, label in enumerate(rev_map.values()):
              ydf[f"__mdb_proba_{label}"] = raw_predictions[:, idx]
  
      # TODO: make this part of the base mixer class? to avoid repetitive code
      #  and ensure other contribs don't accidentally modify the df
      ds.data_frame = original_df
      return ydf[["prediction"]]
  
imports: import time
  from copy import deepcopy
  from typing import Dict, Optional, List
  import numpy
  import pandas
  import torch
  from torch import nn
  from torch.cuda.amp import GradScaler
  from torch.utils.data import DataLoader
  from type_infer.dtype import dtype
  from lightwood.api.types import PredictionArguments
  from lightwood.encoder.base import BaseEncoder
  from lightwood.data.encoded_ds import EncodedDs, ConcatedEncodedDs
  from lightwood.mixer.neural import Neural
  from lightwood.mixer.helpers.ar_net import ArNet
  from lightwood.mixer.helpers.default_net import DefaultNet
  from lightwood.api.types import TimeseriesSettings
label_desc: Use image node_id 5 for calling the NeuralTs obj's underlying member method code with example usage: obj.__call__(ds, args) and returns: ydf