code: def trainModel(params):
      inputs, outputs = sklearn.datasets.load_breast_cancer(True)
  
      startTime = datetime.now()
  
      targets = {
          "first_value": 383,
          "second_value": 862,
          "third_value": 4,
          "fourth_value": 670,
          "fifth_value": 106,
          "sixth_value": 42,
          "seventh_value": 215,
          "eighth_value": 921,
      }
  
      accuracy = 0
      total = 0
      for key in targets.keys():
          accuracy += math.sqrt(
              (params[key] - targets[key])
              * (params[key] - targets[key])
          )
          total += targets[key]
  
      finishTime = datetime.now()
  
      result = {
          "accuracy": accuracy / total,
          "time": (finishTime - startTime).total_seconds(),
      }
      print(result)
      return result
  
imports: import sklearn.datasets
  import sklearn.metrics
  import math
  from datetime import datetime
label_desc: Use image node_id 1 for calling a global function with example usage: trainModel(params) and returns: result