code: def test_progress_display():
      log.info("testing: Progress Display")
      df = pd.read_csv(AIR_FILE, nrows=100)
      df[-20:]
      progress_types = ["bar", "print", "plot", "plot-all", "none"]
      for progress in progress_types:
          m = NeuralProphet(
              epochs=EPOCHS,
              batch_size=BATCH_SIZE,
              learning_rate=LR,
          )
          m.fit(df, progress=progress)
  
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 37 for calling a global function with example usage: test_progress_display() without return types