code: def test_auto_batch_epoch():
      # for epochs = int(2 ** (2.3 * np.log10(100 + n_data)) / (n_data / 1000.0))
      # for epochs = int(2 ** (2.5 * np.log10(100 + n_data)) / (n_data / 1000.0))
      check = {
          "1": (1, 1000),
          "10": (10, 1000),
          "100": (32, 539),
          "1000": (64, 194),
          "10000": (128, 103),
          "100000": (256, 57),
          "1000000": (512, 32),
          "10000000": (1024, 18),
      }
  
      for n_data, (batch_size, epochs) in check.items():
          n_data = int(n_data)
          c = configure.Train(
              learning_rate=None,
              epochs=None,
              batch_size=None,
              loss_func="mse",
              optimizer="SGD",
          )
          c.set_auto_batch_epoch(n_data=n_data)
          assert c.batch_size == batch_size
          assert c.epochs == epochs
  
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 5 for calling a global function with example usage: test_auto_batch_epoch() without return types