code: def compute(self, bottomUpInput, enableLearn, computeInfOutput=None):
      
      super(MonitoredTMShim, self).compute(set(bottomUpInput.nonzero()[0]),
                                           learn=enableLearn)
      numberOfCells = self.numberOfCells()
  
      activeState = numpy.zeros(numberOfCells)
      activeState[self.getActiveCells()] = 1
      self.infActiveState["t"] = activeState
  
      output = numpy.zeros(numberOfCells)
      output[self.getPredictiveCells() + self.getActiveCells()] = 1
      return output
docs: (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