docstr: Call a trained RandomForest mixer to output predictions for the target column.
  
  :param ds: input data with values for all non-target columns.
  :param args: inference-time arguments (e.g. whether to output predicted labels or probabilities).
  
  :return: dataframe with predictions.
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code: def __call__(
      self,
      ds: EncodedDs,
      args: PredictionArguments = PredictionArguments(),
  ) -> pd.DataFrame:
      """
      Call a trained RandomForest mixer to output predictions for the target column.
  
      :param ds: input data with values for all non-target columns.
      :param args: inference-time arguments (e.g. whether to output predicted labels or probabilities).
  
      :return: dataframe with predictions.
      """
      data = ds.get_encoded_data(include_target=False).numpy()
  
      if self.is_classifier:
          predictions = self.model.predict_proba(data)
          decoded_predictions = self.model.classes_.take(
              np.argmax(predictions, axis=1), axis=0
          )
      else:
          predictions = self.model.predict(data)
          if predictions.ndim == 1:
              decoded_predictions = predictions
          else:
              decoded_predictions = self.target_encoder.decode(
                  torch.Tensor(predictions)
              )
  
      if self.positive_domain:
          decoded_predictions = [max(0, p) for p in decoded_predictions]
  
      ydf = pd.DataFrame({"prediction": decoded_predictions})
  
      if args.predict_proba and self.supports_proba:
          for idx, label in enumerate(self.model.classes_):
              ydf[f"__mdb_proba_{label}"] = predictions[:, idx]
  
      return ydf
  
imports: import time
  import math
  import torch
  import numpy
  import pandas
  import optuna
  from typing import Dict, Union, Optional
  from optuna import trial
  from sklearn import clone
  from sklearn.metrics import mean_squared_error
  from sklearn.model_selection import check_cv, cross_val_predict
  from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
  from type_infer.dtype import dtype
  from lightwood.helpers.log import log
  from lightwood.encoder.base import BaseEncoder
  from lightwood.data.encoded_ds import EncodedDs, ConcatedEncodedDs
  from lightwood.mixer.base import BaseMixer
  from lightwood.api.types import PredictionArguments
label_desc: Use image node_id 5 for calling the RandomForest obj's underlying member method code with example usage: obj.__call__(ds, args) and returns: ydf