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: mlflow/examples/sklearn_elasticnet_diabetes/osx/train_diabetes.py
imports: import sys
  import warnings
  from itertools import cycle
  import matplotlib.pyplot
  import numpy
  import pandas
  from sklearn import datasets
  from sklearn.linear_model import ElasticNet, enet_path
  from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
  from sklearn.model_selection import train_test_split
  import mlflow
  import mlflow.sklearn
  from mlflow.models import infer_signature
label_desc: Use image node_id 1 for calling a global function with example usage: eval_metrics(actual, pred) and returns: rmse,  mae,  r2