code: def test_metrics():
      log.info("testing: Plotting")
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      # list
      m_list = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          collect_metrics=["MAE", "MSE", "RMSE"],
      )
      metrics_df = m_list.fit(df, freq="D")
      assert all(
          [
              metric in metrics_df.columns
              for metric in ["MAE", "MSE", "RMSE"]
          ]
      )
      m_list.predict(df)
  
      # dict
      m_dict = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          collect_metrics={"ABC": "MeanSquaredLogError"},
      )
      metrics_df = m_dict.fit(df, freq="D")
      assert "ABC" in metrics_df.columns
      m_dict.predict(df)
  
      # string
      m_string = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          collect_metrics="MAE",
      )
      metrics_df = m_string.fit(df, freq="D")
      assert "MAE" in metrics_df.columns
      m_string.predict(df)
  
      # False
      m_false = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          collect_metrics=False,
      )
      metrics_df = m_false.fit(df, freq="D")
      assert metrics_df is None
      m_false.predict(df)
  
      # None
      m_none = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          collect_metrics=None,
      )
      metrics_df = m_none.fit(df, freq="D")
      assert metrics_df is None
      m_none.predict(df)
  
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 36 for calling a global function with example usage: test_metrics() without return types