code: def test_infer_frequency():
      df = pd.read_csv(PEYTON_FILE, nrows=102)[:50]
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      # Check if freq is set automatically
      df_train, df_test = m.split_df(df)
      log.debug("freq automatically set")
      # Check if freq is set automatically
      df_train, df_test = m.split_df(df, freq=None)
      log.debug("freq automatically set even if set to None")
      # Check if freq is set when equal to the original
      df_train, df_test = m.split_df(df, freq="D")
      log.debug("freq is equal to ideal")
      # Check if freq is set in different freq
      df_train, df_test = m.split_df(df, freq="5D")
      log.debug("freq is set even though is different than the ideal")
      # Assert for data unevenly spaced
      index = np.unique(np.geomspace(1, 40, 20, dtype=int))
      df_uneven = df.iloc[index, :]
      with pytest.raises(ValueError):
          m.split_df(df_uneven)
      # Check if freq is set even in a df with multiple freqs
      df_train, df_test = m.split_df(df_uneven, freq="H")
      log.debug("freq is set even with not definable freq")
      # Check if freq is set for list
      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,
      )
      m.fit(df_global)
      log.debug("freq is set for list of dataframes")
      # Check if freq is set for list with different freq for n_lags=0
      time_range = pd.date_range(
          start="1994-12-01", periods=df.shape[0], freq="M"
      )
      df1["ds"] = time_range
      df_global = pd.concat((df1, df2))
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_lags=0,
      )
      m.fit(df_global)
      log.debug("freq is set for list of dataframes(n_lags=0)")
      # Assert for automatic frequency in list with different freq
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_lags=2,
      )
      with pytest.raises(ValueError):
          m.fit(df_global)
      # Exceptions
      frequencies = ["M", "MS", "Y", "YS", "Q", "QS", "B", "BH"]
      df = df.iloc[:200, :]
      for freq in frequencies:
          df1 = df.copy(deep=True)
          time_range = pd.date_range(
              start="1994-12-01", periods=df.shape[0], freq=freq
          )
          df1["ds"] = time_range
          df_train, df_test = m.split_df(df1)
      log.debug("freq is set for all the exceptions")
  
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 12 for calling a global function with example usage: test_infer_frequency() without return types