code: def test_custom_torch_loss():
      log.info("TEST PyTorch Custom Loss")
  
      class MyLoss(torch.nn.modules.loss._Loss):
          def forward(self, input, target):
              alpha = 0.9
              y_diff = target - input
              yhat_diff = input - target
              loss = (
                  (
                      alpha
                      * torch.max(y_diff, torch.zeros_like(y_diff))
                      + (1 - alpha)
                      * torch.max(
                          yhat_diff, torch.zeros_like(yhat_diff)
                      )
                  )
                  .sum()
                  .mean()
              )
              return loss
  
      df = pd.read_csv(YOS_FILE, nrows=NROWS)
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          loss_func=MyLoss,
      )
      m.fit(df, freq="5min")
      future = m.make_future_dataframe(
          df, periods=12, n_historic_predictions=12
      )
      m.predict(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 24 for calling a global function with example usage: test_custom_torch_loss() and returns: loss