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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 isObjectClassified(self, objectName, minOverlap=None, maxL2Size=None):
 """ Return True if objectName is currently unambiguously classified by every L2 column. Classification is correct and unambiguous if the current L2 overlap with the true object is greater than minOverlap and if the size of the L2 representation is no more than maxL2Size :param minOverlap: min overlap to consider the object as recognized. Defaults to half of the SDR size :param maxL2Size: max size for the L2 representation Defaults to 1.5 * SDR size :return: True/False """

    L2Representation = self.getL2Representations()
    objectRepresentation = self.objectL2Representations[objectName]
    sdrSize = self.config["L2Params"]["sdrSize"]
    if minOverlap is None:
      minOverlap = sdrSize / 2
    if maxL2Size is None:
      maxL2Size = 1.5*sdrSize

    numCorrectClassifications = 0
    for col in xrange(self.numColumns):
      overlapWithObject = len(objectRepresentation[col] & L2Representation[col])

      if ( overlapWithObject >= minOverlap  and
           len(L2Representation[col]) <= maxL2Size ):
        numCorrectClassifications += 1

    return numCorrectClassifications == self.numColumns