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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 learnSequences(self, sequences):
 """ Learns all provided sequences. Always reset the network in between sequences. Sequences format: sequences = [ [ set([16, 22, 32]), # S0, position 0 set([13, 15, 33]) # S0, position 1 ], [ set([6, 12, 52]), # S1, position 0 set([6, 2, 15]) # S1, position 1 ], ] Note that the order of each sequence is important. It denotes the sequence number and will be used during inference to plot accuracy. Parameters: @param sequences (list) Sequences to learn, in the canonical format specified above """

    # This method goes through four phases:
    #   1) We first train L4 on the sequences, over multiple passes
    #   2) We then train L2 in one pass.
    #   3) We then continue training on L4 so the apical segments learn
    #   4) We run inference to store L2 representations for each sequence
    # retrieve L2 representations

    # print "1) Train L4 sequence memory"

    # We're now using online learning, so both layers should be trying to learn
    # at all times.

    sequence_order = range(len(sequences))
    if self.config["L2Params"]["onlineLearning"]:
      # Train L2 and L4
      self._setLearningMode(l4Learning=True, l2Learning=True)
      for _ in xrange(self.numLearningPoints):
        random.shuffle(sequence_order)
        for i in sequence_order:
          sequence = sequences[i]
          for s in sequence:
            self.sensorInputs[0].addDataToQueue(list(s), 0, 0)
            self.network.run(1)

          # This is equivalent to, and faster than, giving the network no input
          # for a period of time.
          self.sendReset()
    else:
      # Train L4
      self._setLearningMode(l4Learning=True, l2Learning=False)
      for _ in xrange(self.numLearningPoints):
        random.shuffle(sequence_order)
        for i in sequence_order:
          sequence = sequences[i]
          for s in sequence:
            self.sensorInputs[0].addDataToQueue(list(s), 0, 0)
            self.network.run(1)

          # This is equivalent to, and faster than, giving the network no input
          # for a period of time.
          self.sendReset()

      # Train L2
      self._setLearningMode(l4Learning=False, l2Learning=True)
      for i in sequence_order:
        sequence = sequences[i]
        for s in sequence:
          self.sensorInputs[0].addDataToQueue(list(s), 0, 0)
          self.network.run(1)
        self.sendReset()

      # Train L4 apical segments
      self._setLearningMode(l4Learning=True, l2Learning=False)
      for _ in xrange(5):
        for i in sequence_order:
          sequence = sequences[i]
          for s in sequence:
            self.sensorInputs[0].addDataToQueue(list(s), 0, 0)
            self.network.run(1)
          self.sendReset()

    self._setLearningMode(l4Learning=False, l2Learning=False)
    self.sendReset()
    for sequenceNum, sequence in enumerate(sequences):
      for s in sequence:
        self.sensorInputs[0].addDataToQueue(list(s), 0, 0)
        self.network.run(1)
      self.objectL2Representations[sequenceNum] = self.getL2Representations()
      self.sendReset()
    return