code: def test_float32_inputs():
      # test if float32 inputs are forecasted as float32 outputs
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      df["y"] = df["y"].astype(np.float32)
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      m.fit(df, freq="D")
      forecast = m.predict(df)
      assert forecast["yhat1"].dtype == np.float32
  
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 26 for calling a global function with example usage: test_float32_inputs() without return types