def dummyModelParams(perm):
errScore = <NUM_LIT:50><EOL>waitTime = <NUM_LIT><EOL>dummyModelParams = dict(<EOL>metricValue = errScore,<EOL>iterations = int(os.environ.get('<STR_LIT>', '<STR_LIT:5>')),<EOL>waitTime = waitTime,<EOL>sysExitModelRange = os.environ.get('<STR_LIT>',<EOL>None),<EOL>delayModelRange = os.environ.get('<STR_LIT>',<EOL>None),<EOL>errModelRange = os.environ.get('<STR_LIT>',<EOL>None),<EOL>jobFailErr = bool(os.environ.get('<STR_LIT>', False))<EOL>)<EOL>return dummyModelParams<EOL>
This function can be used for Hypersearch algorithm development. When
    present, Hypersearch doesn't actually run the CLA model in the OPF, but instead run
    a dummy model. This function returns the dummy model params that will be
    used. See the OPFDummyModelRunner class source code (in
    nupic.swarming.ModelRunner) for a description of the schema for
    the dummy model params.