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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 create_from_boosted_machine(self, boosted_machine, classifiers_per_round, classification_thresholds=-5.):
 """Creates this cascade from the given boosted machine, by simply splitting off strong classifiers that have classifiers_per_round weak classifiers. **Parameters:** ``boosted_machine`` : :py:class:`bob.learn.boosting.BoostedMachine` The strong classifier to split into a regular cascade. ``classifiers_per_round`` : int The number of classifiers that each cascade step should contain. ``classification_threshold`` : float A single threshold that will be applied in all rounds of the cascade. """

    indices = list(range(0, len(boosted_machine.weak_machines), classifiers_per_round))
    if indices[-1] != len(boosted_machine.weak_machines): indices.append(len(boosted_machine.weak_machines))
    self.cascade = []
    self.indices = []
    for i in range(len(indices)-1):
      machine = bob.learn.boosting.BoostedMachine()
      for index in range(indices[i], indices[i+1]):
        machine.add_weak_machine(boosted_machine.weak_machines[index], boosted_machine.weights[index, 0])
      self.cascade.append(machine)
    if isinstance(classification_thresholds, (int, float)):
      self.thresholds = [classification_thresholds] * len(self.cascade)
    else:
      self.thresholds = classification_thresholds