code: def test_time_dataset():
      # manually load any file that stores a time series, for example:
      df_in = pd.read_csv(AIR_FILE, index_col=False, nrows=NROWS)
      log.debug(f"Infile shape: {df_in.shape}")
      n_lags = 3
      n_forecasts = 1
      valid_p = 0.2
      config_missing = configure.MissingDataHandling()
      df_train, df_val = df_utils.split_df(
          df_in, n_lags, n_forecasts, valid_p
      )
      # create a tabularized dataset from time series
      df, _, _ = df_utils.check_dataframe(df_train)
      local_data_params, global_data_params = df_utils.init_data_params(
          df=df, normalize="minmax"
      )
      df = df.drop("ID", axis=1)
      df = df_utils.normalize(df, global_data_params)
      inputs, targets, _ = time_dataset.tabularize_univariate_datetime(
          df,
          n_lags=n_lags,
          n_forecasts=n_forecasts,
          config_missing=config_missing,
      )
      log.debug(
          "tabularized inputs: {}".format(
              "; ".join(
                  [
                      "{}: {}".format(inp, values.shape)
                      for inp, values in inputs.items()
                  ]
              )
          )
      )
  
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 2 for calling a global function with example usage: test_time_dataset() without return types