code: def test_dataloader():
      df = pd.read_csv(PEYTON_FILE, nrows=100)
      df["A"] = np.arange(len(df))
      df["B"] = np.arange(len(df)) * 0.1
      df1 = df[:50]
      df1 = df1.assign(ID="df1")
      df2 = df[50:]
      df2 = df2.assign(ID="df2")
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          yearly_seasonality=True,
          weekly_seasonality=True,
          daily_seasonality=True,
          n_lags=3,
          n_forecasts=2,
      )
      m.add_future_regressor("A")
      m.add_lagged_regressor("B")
      config_normalization = configure.Normalization(
          "auto", False, True, False
      )
      df_global = pd.concat((df1, df2))
      df_global["ds"] = pd.to_datetime(df_global.loc[:, "ds"])
      config_normalization.init_data_params(
          df_global,
          m.config_lagged_regressors,
          m.config_regressors,
          m.config_events,
      )
      m.config_normalization = config_normalization
      df_global = _normalize(
          df=df_global, config_normalization=m.config_normalization
      )
      dataset = _create_dataset(m, df_global, predict_mode=False)
      loader = DataLoader(
          dataset,
          batch_size=min(1024, len(df)),
          shuffle=True,
          drop_last=False,
      )
      for inputs, targets, meta in loader:
          assert set(meta["df_name"]) == set(df_global["ID"].unique())
          break
  
imports: import logging
  import os
  import pathlib
  import matplotlib.pyplot
  import numpy
  import pandas
  import pytest
  from torch.utils.data import DataLoader
  from neuralprophet import NeuralProphet, configure, df_utils, time_dataset
  from neuralprophet.data.process import _create_dataset, _handle_missing_data
  from neuralprophet.data.transform import _normalize
label_desc: Use image node_id 14 for calling a global function with example usage: test_dataloader() without return types