code: def custom_metric(eval_df, _builtin_metrics):
      return np.sum(
          np.abs(eval_df["prediction"] - eval_df["target"] + 1) ** 2
      )
  
file_path: mlflow/examples/evaluation/evaluate_with_custom_metrics_comprehensive.py
imports: import matplotlib.pyplot
  import numpy
  import pandas
  from sklearn.datasets import fetch_california_housing
  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import train_test_split
  import mlflow
  from mlflow.models import infer_signature, make_metric
label_desc: Use image node_id 1 for calling a global function with example usage: custom_metric(eval_df, _builtin_metrics) and returns: np