docstr: The RandomForest mixer does not support updates. If the model does not exist, a new one will be created and fitted. 
  
  :param train_data: encoded features for (new) training dataset
  :param dev_data: encoded features for (new) dev dataset
docstr_tok: ["The","RandomForest","mixer","does","not","support","updates",".","If","the","model","does","not","exist",",","a","new","one","will","be","created","and","fitted",".",":","param","train_data",":","encoded","features","for","(","new",")","training","dataset",":","param","dev_data",":","encoded","features","for","(","new",")","dev","dataset"]
code: def partial_fit(
      self,
      train_data: EncodedDs,
      dev_data: EncodedDs,
      args: Optional[dict] = None,
  ) -> None:
      """
      The RandomForest mixer does not support updates. If the model does not exist, a new one will be created and fitted.
  
      :param train_data: encoded features for (new) training dataset
      :param dev_data: encoded features for (new) dev dataset
      """  # noqa
      if self.model is None:
          self.fit(train_data, dev_data)
  
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 4 for calling the RandomForest obj's underlying member method code with example usage: obj.partial_fit(train_data, dev_data, args) without return types