class_docstr: This class represents a single execution of a model with a given set of hyper-parameters. It takes care of
  of managing the model process and standardizing the results.
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docstr: Initialize this execution with the given configuration and the given parameters.
  
  :param config: The Execution configuration. See configurationSchema() or the README file for more information.
  :param parameters: The hyper parameters for this model.
  :param worker_n: When executing models in parallel, this defines which worker this execution is.
docstr_tok: ["Initialize","this","execution","with","the","given","configuration","and","the","given","parameters",".",":","param","config",":","The","Execution","configuration",".","See","configurationSchema","(",")","or","the","README","file","for","more","information",".",":","param","parameters",":","The","hyper","parameters","for","this","model",".",":","param","worker_n",":","When","executing","models","in","parallel",",","this","defines","which","worker","this","execution","is","."]
code: def __init__(self, config, parameters, worker_n=0):
      """
      Initialize this execution with the given configuration and the given parameters.
  
      :param config: The Execution configuration. See configurationSchema() or the README file for more information.
      :param parameters: The hyper parameters for this model.
      :param worker_n: When executing models in parallel, this defines which worker this execution is.
      """
      # jsonschema.validate(config, self.configurationSchema())
  
      # Perform one additional validation
      if (
          "auto_kill_max_time" in config
          or "auto_kill_max_ram" in config
          or "auto_kill_max_system_ram" in config
      ) and ("auto_kill_loss" not in config):
          raise ValueError(
              "Configuration for model execution has an auto_kill parameter, but is missing the auto_kill_loss. Please set an auto_kill_loss to use automatic kill."
          )
  
      self.config = config
      self.parameters = parameters
  
      self.process = None
      self.result = None
      self.startTime = None
      self.endTime = None
      self.killed = False
      self.worker_n = worker_n
      self.scriptToken = None
  
file_path: hypermax/hypermax/execution.py
imports: import subprocess
  import json
  import atexit
  import jsonschema
  import psutil
  import sys
  import random
  import time
  import datetime
  import os
label_desc: Use image node_id 1 to create a new Execution object  with example: obj = Execution(config, parameters, worker_n)