def dummyModelParams(perm):
<EOL>errScore = <NUM_LIT:50><EOL>if perm['<STR_LIT>']['<STR_LIT>']['<STR_LIT>']['<STR_LIT>'] is not None:<EOL><INDENT>errScore += <NUM_LIT:10><EOL><DEDENT>if perm['<STR_LIT>']['<STR_LIT>']['<STR_LIT>']['<STR_LIT:address>'] is not None:<EOL><INDENT>errScore -= <NUM_LIT:10><EOL><DEDENT>if perm['<STR_LIT>']['<STR_LIT>']['<STR_LIT>']['<STR_LIT>'] is not None:<EOL><INDENT>errScore -= <NUM_LIT:20><EOL><DEDENT>if perm['<STR_LIT>']['<STR_LIT>']['<STR_LIT>']['<STR_LIT>']is not None:<EOL><INDENT>errScore -= <NUM_LIT:30><EOL><DEDENT>if perm['<STR_LIT>']['<STR_LIT>']['<STR_LIT>']['<STR_LIT>']is not None:<EOL><INDENT>errScore -= <NUM_LIT><EOL><DEDENT>dummyModelParams = dict(<EOL>metricValue = errScore,<EOL>iterations = int(os.environ.get('<STR_LIT>', '<STR_LIT:1>')),<EOL>waitTime = None,<EOL>sysExitModelRange = 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 runs 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.