code: def eval_metrics(actual, pred):
      rmse = np.sqrt(mean_squared_error(actual, pred))
      mae = mean_absolute_error(actual, pred)
      r2 = r2_score(actual, pred)
      return rmse, mae, r2
  
file_path: MachineLearningNotebooks/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train.py
imports: import os
  import warnings
  import sys
  import pandas
  import numpy
  from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import ElasticNet
  import mlflow
  import mlflow.sklearn
label_desc: Use image node_id 1 for calling a global function with example usage: eval_metrics(actual, pred) and returns: rmse,  mae,  r2