code: def _prepare_dataframe_to_predict(
      model, df: pd.DataFrame, max_lags: int, freq: Optional[str]
  ) -> pd.DataFrame:
      """
      Pre-processes a dataframe for prediction using the specified model.
  
      Parameters
      ----------
          model:
              The NeuralProphet model
          df: pd.DataFrame
              dataframe containing column ``ds``, ``y``, and optionally``ID`` with all data
          max_lags: int
              The maximum number of lags to include in the output dataframe.
          freq: str
              data step sizes. Frequency of data recording,
  
      Returns
      ----------
          pd.DataFrame
              pre-processed dataframe
  
      Raises
      ----------
          ValueError
          If the input dataframe has already been normalized, if there is insufficient input data for prediction,
          if only datestamps are provided but y values are needed for auto-regression.
      """
      # Receives df with ID column
      df_prepared = pd.DataFrame()
      for df_name, df_i in df.groupby("ID"):
          df_i = df_i.copy(deep=True)
          _ = df_utils.infer_frequency(df_i, n_lags=max_lags, freq=freq)
          # check if received pre-processed df
          if "y_scaled" in df_i.columns or "t" in df_i.columns:
              raise ValueError(
                  "DataFrame has already been normalized. "
                  "Please provide raw dataframe or future dataframe."
              )
          # Checks
          if len(df_i) == 0 or len(df_i) < max_lags:
              raise ValueError(
                  "Insufficient input data for a prediction."
                  "Please supply historic observations (number of rows) of at least max_lags (max of number of n_lags)."
              )
          if len(df_i.columns) == 1 and "ds" in df_i:
              if max_lags != 0:
                  raise ValueError(
                      "only datestamps provided but y values needed for auto-regression."
                  )
              df_i = _check_dataframe(
                  model, df_i, check_y=False, exogenous=False
              )
          else:
              df_i = _check_dataframe(
                  model,
                  df_i,
                  check_y=model.max_lags > 0,
                  exogenous=False,
              )
              # fill in missing nans except for nans at end
              df_i = _handle_missing_data(
                  df=df_i,
                  freq=freq,
                  n_lags=model.n_lags,
                  n_forecasts=model.n_forecasts,
                  config_missing=model.config_missing,
                  config_regressors=model.config_regressors,
                  config_lagged_regressors=model.config_lagged_regressors,
                  config_events=model.config_events,
                  config_seasonality=model.config_seasonality,
                  predicting=True,
              )
          df_prepared = pd.concat(
              (
                  df_prepared,
                  df_i.copy(deep=True).reset_index(drop=True),
              ),
              ignore_index=True,
          )
      return df_prepared
  
file_path: neural_prophet/neuralprophet/data/process.py
imports: import logging
  from typing import List, Optional
  import numpy
  import pandas
  from neuralprophet import df_utils, time_dataset
  from neuralprophet.configure import ConfigCountryHolidays, ConfigEvents, ConfigFutureRegressors, ConfigLaggedRegressors, ConfigSeasonality
  from neuralprophet.np_types import Components
label_desc: Use image node_id 3 for calling a global function with example usage: _prepare_dataframe_to_predict(model, df, max_lags, freq) and returns: df_prepared