code: def test_get_latest_forecast():
      log.info("testing: get_latest_forecast")
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_forecasts=24,
          n_lags=36,
          changepoints_range=0.95,
          n_changepoints=30,
          weekly_seasonality=False,
      )
      m.fit(df)
      forecast = m.predict(df)
      m.get_latest_forecast(
          forecast,
          df_name=None,
          include_history_data=None,
          include_previous_forecasts=5,
      )
      m.get_latest_forecast(
          forecast,
          include_history_data=False,
          include_previous_forecasts=5,
      )
      m.get_latest_forecast(
          forecast,
          include_history_data=True,
          include_previous_forecasts=5,
      )
      help(m.get_latest_forecast)
      log.info("testing: get_latest_forecast with n_lags=0")
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_forecasts=24,
          n_lags=0,
          changepoints_range=0.95,
          n_changepoints=30,
          weekly_seasonality=False,
      )
      m.fit(df)
      forecast = m.predict(df)
      with pytest.raises(Exception):
          m.get_latest_forecast(
              forecast,
              include_history_data=None,
              include_previous_forecasts=5,
          )
  
      df1 = df.copy(deep=True)
      df1["ID"] = "df1"
      df2 = df.copy(deep=True)
      df2["ID"] = "df2"
      df_global = pd.concat((df1, df2))
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_forecasts=24,
          n_lags=36,
          changepoints_range=0.95,
          n_changepoints=30,
          weekly_seasonality=False,
      )
      m.fit(df_global, freq="D")
      future = m.make_future_dataframe(
          df_global, periods=m.n_forecasts, n_historic_predictions=10
      )
      forecast = m.predict(future)
      log.info("Plot forecast with many IDs - Raise exceptions")
      forecast = m.predict(df_global)
      m.get_latest_forecast(
          forecast,
          df_name="df1",
          include_history_data=None,
          include_previous_forecasts=5,
      )
      with pytest.raises(Exception):
          m.get_latest_forecast(forecast, include_previous_forecasts=10)
  
      log.info("Not enough values in df")
      df3 = df.iloc[:10].copy(deep=True)
      df3["ID"] = "df3"
      with pytest.raises(Exception):
          m.fit(df3, freq="D")
  
imports: import logging
  import math
  import os
  import pathlib
  import matplotlib.pyplot
  import numpy
  import pandas
  import pytest
  import torch
  from neuralprophet import NeuralProphet, df_utils, set_random_seed
  from neuralprophet.data.process import _handle_missing_data, _validate_column_name
label_desc: Use image node_id 35 for calling a global function with example usage: test_get_latest_forecast() without return types