class_docstr: Evaluate metrics for y_true and y_pred.
code: def check_metric(metric):
      if not metric:
          raise ValueError(f"Got invalid metric name of {metric}!")
      if metric not in Evaluator.metrics_func.keys():
          raise ValueError("metric " + metric + " is not supported")
  
imports: from sklearn.metrics import mean_squared_error
  from sklearn.metrics import r2_score
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_log_error
  import numpy
  import pandas
label_desc: Use image node_id 2 for calling the Evaluator obj's underlying member method code with example usage: obj.check_metric(metric) without return types