def compute(self, bottomUpInput, enableLearn, computeInfOutput=None):
super(TMShimMixin, self).compute(set(bottomUpInput.nonzero()[<NUM_LIT:0>]),<EOL>learn=enableLearn)<EOL>numberOfCells = self.numberOfCells()<EOL>activeState = numpy.zeros(numberOfCells)<EOL>activeState[self.getActiveCells()] = <NUM_LIT:1><EOL>self.infActiveState["<STR_LIT:t>"] = activeState<EOL>output = numpy.zeros(numberOfCells)<EOL>output[self.getPredictiveCells()] = <NUM_LIT:1><EOL>output[self.getActiveCells()] = <NUM_LIT:1><EOL>return output<EOL>
(From `backtracking_tm.py`)
Handle one compute, possibly learning.

@param bottomUpInput     The bottom-up input, typically from a spatial pooler
@param enableLearn       If true, perform learning
@param computeInfOutput  If None, default behavior is to disable the inference
                         output when enableLearn is on.
                         If true, compute the inference output
                         If false, do not compute the inference output