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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def burstColumn(self, column, columnMatchingSegments, prevActiveCells, prevWinnerCells, learn):
 """ Activates all of the cells in an unpredicted active column, chooses a winner cell, and, if learning is turned on, learns on one segment, growing a new segment if necessary. @param column (int) Index of bursting column. @param columnMatchingSegments (iter) Matching segments in this column, or None if there aren't any. @param prevActiveCells (list) Active cells in `t-1`. @param prevWinnerCells (list) Winner cells in `t-1`. @param learn (bool) Whether or not learning is enabled. @return (tuple) Contains: `cells` (iter), `winnerCell` (int), """


    start = self.cellsPerColumn * column

    # Strip out destroyed cells before passing along to base _burstColumn()
    cellsForColumn = [cellIdx
                      for cellIdx
                      in xrange(start, start + self.cellsPerColumn)
                      if cellIdx not in self.deadCells]

    return self._burstColumn(
      self.connections, self._random, self.lastUsedIterationForSegment, column,
      columnMatchingSegments, prevActiveCells, prevWinnerCells, cellsForColumn,
      self.numActivePotentialSynapsesForSegment, self.iteration,
      self.maxNewSynapseCount, self.initialPermanence, self.permanenceIncrement,
      self.permanenceDecrement, self.maxSegmentsPerCell,
      self.maxSynapsesPerSegment, learn)