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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 learnObject(self, objectDescription, randomLocation=False, useNoise=False, noisyTrainingTime=1):
 """ Train the network to recognize the specified object. Move the sensor to one of its features and activate a random location representation in the location layer. Move the sensor over the object, updating the location representation through path integration. At each point on the object, form reciprocal connections between the represention of the location and the representation of the sensory input. @param objectDescription (dict) For example: {"name": "Object 1", "features": [{"top": 0, "left": 0, "width": 10, "height": 10, "name": "A"}, {"top": 0, "left": 10, "width": 10, "height": 10, "name": "B"}]} @return locationsAreUnique (bool) True if this object was assigned a unique set of locations. False if a location on this object has the same location representation as another location somewhere else. """

    self.reset()
    self.column.activateRandomLocation()

    locationsAreUnique = True

    if randomLocation or useNoise:
      numIters = noisyTrainingTime
    else:
      numIters = 1
    for i in xrange(numIters):
      for iFeature, feature in enumerate(objectDescription["features"]):
        self._move(feature, randomLocation=randomLocation, useNoise=useNoise)
        featureSDR = self.features[feature["name"]]
        self._sense(featureSDR, learn=True, waitForSettle=False)

        locationRepresentation = self.column.getSensoryAssociatedLocationRepresentation()
        self.locationRepresentations[(objectDescription["name"],
                                      iFeature)].append(locationRepresentation)
        self.inputRepresentations[(objectDescription["name"],
                                   iFeature, feature["name"])] = (
                                     self.column.L4.getWinnerCells())

        locationTuple = tuple(locationRepresentation)
        locationsAreUnique = (locationsAreUnique and
                              locationTuple not in self.representationSet)

        self.representationSet.add(tuple(locationRepresentation))

    self.learnedObjects.append(objectDescription)

    return locationsAreUnique