code: def generate_scenario(ss_content):
      """
      Generate the scenario. The scenario-object (smac.scenario.scenario.Scenario) is used to configure SMAC and
      can be constructed either by providing an actual scenario-object, or by specifing the options in a scenario file.
      Reference: https://automl.github.io/SMAC3/stable/options.html
      The format of the scenario file is one option per line:
      OPTION1 = VALUE1
      OPTION2 = VALUE2
      ...
      Parameters
      ----------
      abort_on_first_run_crash: bool
          If true, SMAC will abort if the first run of the target algorithm crashes. Default: True,
          because trials reported to nni tuner would always in success state
      algo: function
          Specifies the target algorithm call that SMAC will optimize. Interpreted as a bash-command.
          Not required by tuner, but required by nni's training service for running trials
      always_race_default:
          Race new incumbents always against default configuration
      cost_for_crash:
          Defines the cost-value for crashed runs on scenarios with quality as run-obj. Default: 2147483647.0.
          Trials reported to nni tuner would always in success state
      cutoff_time:
          Maximum runtime, after which the target algorithm is cancelled. `Required if *run_obj* is runtime`
      deterministic: bool
          If true, the optimization process will be repeatable.
      execdir:
          Specifies the path to the execution-directory. Default: .
          Trials are executed by nni's training service
      feature_file:
          Specifies the file with the instance-features.
          No features specified or feature file is not supported
      initial_incumbent:
          DEFAULT is the default from the PCS. Default: DEFAULT. Must be from: [‘DEFAULT’, ‘RANDOM’].
      input_psmac_dirs:
          For parallel SMAC, multiple output-directories are used.
          Parallelism is supported by nni
      instance_file:
          Specifies the file with the training-instances. Not supported
      intensification_percentage:
          The fraction of time to be used on intensification (versus choice of next Configurations). Default: 0.5.
          Not supported, trials are controlled by nni's training service and kill be assessor
      maxR: int
          Maximum number of calls per configuration. Default: 2000.
      memory_limit:
          Maximum available memory the target algorithm can occupy before being cancelled.
      minR: int
          Minimum number of calls per configuration. Default: 1.
      output_dir:
          Specifies the output-directory for all emerging files, such as logging and results.
          Default: smac3-output_2018-01-22_15:05:56_807070.
      overall_obj:
          PARX, where X is an integer defining the penalty imposed on timeouts (i.e. runtimes that exceed the cutoff-time).
          Timeout is not supported
      paramfile:
          Specifies the path to the PCS-file.
      run_obj:
          Defines what metric to optimize. When optimizing runtime, cutoff_time is required as well.
          Must be from: [‘runtime’, ‘quality’].
      runcount_limit: int
          Maximum number of algorithm-calls during optimization. Default: inf.
          Use default because this is controlled by nni
      shared_model:
          Whether to run SMAC in parallel mode. Parallelism is supported by nni
      test_instance_file:
          Specifies the file with the test-instances. Instance is not supported
      tuner-timeout:
          Maximum amount of CPU-time used for optimization. Not supported
      wallclock_limit: int
          Maximum amount of wallclock-time used for optimization. Default: inf.
          Use default because this is controlled by nni
  
      Returns
      -------
      Scenario:
          The scenario-object (smac.scenario.scenario.Scenario) is used to configure SMAC and can be constructed
          either by providing an actual scenario-object, or by specifing the options in a scenario file
      """
      with open("scenario.txt", "w") as sce_fd:
          sce_fd.write("deterministic = 0\n")
          # sce_fd.write('output_dir = \n')
          sce_fd.write("paramfile = param_config_space.pcs\n")
          sce_fd.write("run_obj = quality\n")
  
      return generate_pcs(ss_content)
  
imports: import json
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: generate_scenario(ss_content) and returns: generate_pcs