code: def test_global_modeling_validation_df():
      log.info("Global Modeling + Local Normalization")
      df = pd.read_csv(PEYTON_FILE, nrows=512)
      df1_0 = df.iloc[:128, :].copy(deep=True)
      df1_0["ID"] = "df1"
      df2_0 = df.iloc[128:256, :].copy(deep=True)
      df2_0["ID"] = "df2"
      df3_0 = df.iloc[256:384, :].copy(deep=True)
      df_global = pd.concat((df1_0, df2_0))
      m = NeuralProphet(
          n_forecasts=2,
          n_lags=10,
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      with pytest.raises(ValueError):
          m.fit(df_global, freq="D", validation_df=df3_0)
      log.info("Error - name of validation df was not provided")
      m = NeuralProphet(
          n_forecasts=2,
          n_lags=10,
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      m.fit(df_global, freq="D", validation_df=df2_0)
  
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 27 for calling a global function with example usage: test_global_modeling_validation_df() without return types