code: def test_drop_missing_values_after_imputation():
      m1 = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_lags=12,
          n_forecasts=12,
          weekly_seasonality=True,
          impute_missing=True,
          impute_linear=10,
          impute_rolling=0,
          drop_missing=True,
      )
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      log.info("introducing two large NaN windows")
      df.loc[100:131, "y"] = np.nan
      df.loc[170:200, "y"] = np.nan
      m1.fit(df, freq="D", validation_df=None)
      future = m1.make_future_dataframe(
          df, periods=60, n_historic_predictions=60
      )
      m1.predict(df=df)
      m1.predict(df=future)
  
      log.info(
          "Testing drop of remaining values after lin imputation, no lags"
      )
      m2 = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_lags=0,
          n_forecasts=12,
          weekly_seasonality=True,
          impute_missing=True,
          impute_linear=10,
          impute_rolling=0,
          drop_missing=True,
      )
      m2.fit(df, freq="D", validation_df=None)
      m2.predict(df=df)
      future = m2.make_future_dataframe(
          df, periods=60, n_historic_predictions=60
      )
      m2.predict(df=future)
  
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 39 for calling a global function with example usage: test_drop_missing_values_after_imputation() without return types