code: def predict(
      self, df: pd.DataFrame, args: Optional[Dict] = None
  ) -> pd.DataFrame:
      rt_df = df.copy(deep=True)
      # read model and args from storage
      model_bytes = self.model_storage.file_get(
          self.PERSIST_MODEL_FILE_NAME
      )
      deserialize_args = self.model_storage.json_get(
          self.PERSIST_ARGS_KEY_IN_JSON_STORAGE
      )
  
      # resolve args
      task = deserialize_args[self.ARG_USING_TASK]
      model_type = deserialize_args[self.ARG_USING_MODEL_TYPE]
      time_column = deserialize_args[self.ARG_TIME_COLUMN]
      target = deserialize_args[self.ARG_TARGET]
      horizon = deserialize_args[self.ARG_PREDICT_HORIZON]
      feature_column_sequence = list(
          deserialize_args[self.ARG_COLUMN_SEQUENCE]
      )
      task_enum = TaskType[task]
  
      # check df and prepare data
      if task_enum == TaskType.forecast:
          feature_column_sequence.remove(target)
      missing_required_columns = set(feature_column_sequence) - set(
          rt_df.columns.values
      )
      if len(missing_required_columns) > 0:
          raise Exception(
              "Missing required columns: "
              + ",".join(missing_required_columns)
          )
      feature_df = rt_df[feature_column_sequence]
      ts_feature_df, _ = to_ts_dataframe(
          df=feature_df, time_col=time_column
      )
  
      # init model adapter
      adapter_class: BaseMerlionForecastAdapter = (
          self.__args_to_adapter_class(task=task, model_type=model_type)
      )
      model_args = {}
      if task_enum == TaskType.forecast:
          model_args[
              MerlionArguments.max_forecast_steps.value
          ] = horizon
      adapter = adapter_class(**model_args)
      adapter.initialize_model(bytes=model_bytes)
  
      # predict
      pred_df = adapter.predict(df=ts_feature_df, target=target)
  
      # build result
      pred_df = ts_feature_df[[]].join(pred_df, how="left")
  
      # arrange data
      pred_df.index = rt_df.index
      if task_enum == TaskType.forecast:
          pred_df = pred_df[~pred_df[target].isna()]
          rt_df.drop(columns=[target], inplace=True)
      elif task_enum == TaskType.detector:
          pred_df[f"{target}__anomaly_score"].fillna(0, inplace=True)
      rt_df = rt_df.join(pred_df, how="right")
      return rt_df
  
imports: from enum import Enum
  import json
  from typing import Optional, Dict
  import numpy
  import pandas
  import copy
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.utilities import log
  from .adapters import BaseMerlionForecastAdapter, DefaultForecasterAdapter, MerlionArguments, DefaultDetectorAdapter, SarimaForecasterAdapter, ProphetForecasterAdapter, MSESForecasterAdapter, IsolationForestDetectorAdapter, WindStatsDetectorAdapter, ProphetDetectorAdapter
label_desc: Use image node_id 2 for calling the MerlionHandler obj's underlying member method code with example usage: obj.predict(df, args) and returns: rt_df