code: def convert_dates(
      index, df, horizon, freq, seasonality, dataset, group
  ):
      if dataset == "M4" and group == "Yearly":
          # yearly dataset has too long series
          # see https://eng.uber.com/m4-forecasting-competition/
          df = df.tail(60)
      df["ds"] = pd.date_range(
          end="2018-01-01", periods=df.shape[0], freq=freq
      )
      return df
  
file_path: statsforecast/experiments/neuralprophet/src/neuralprophet.py
imports: import os
  import time
  from functools import partial
  from multiprocessing import Pool, cpu_count
  import fire
  import numpy
  import pandas
  from neuralprophet import NeuralProphet
  from src.data import get_data
label_desc: Use image node_id 1 for calling a global function with example usage: convert_dates(index, df, horizon, freq, seasonality, dataset, group) and returns: df