code: def partial_fit(
      self,
      train_data: EncodedDs,
      dev_data: EncodedDs,
      args: Optional[dict] = None,
  ) -> None:
      # TODO: https://xgboost.readthedocs.io/en/stable/python/python_api.html#module-xgboost.sklearn
      # To resume training from a previous checkpoint, explicitly pass xgb_model argument.
      log.info(
          "XGBoost mixer does not have a `partial_fit` implementation"
      )
  
imports: from typing import Dict, List, Optional, Union
  import time
  import torch
  import optuna
  import xgboost
  import numpy
  import pandas
  from sklearn.preprocessing import OrdinalEncoder
  from type_infer.dtype import dtype
  from lightwood.helpers.log import log
  from lightwood.helpers.parallelism import get_nr_procs
  from lightwood.mixer.base import BaseMixer
  from lightwood.encoder.base import BaseEncoder
  from lightwood.api.types import PredictionArguments
  from lightwood.data.encoded_ds import EncodedDs
label_desc: Use image node_id 4 for calling the XGBoostMixer obj's underlying member method code with example usage: obj.partial_fit(train_data, dev_data, args) without return types