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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 train(self, X):
 """ Trains multiple logistic regression classifiers to handle the multiclass problem posed by ``X`` X (numpy.ndarray):
 The input data matrix. This must be a numpy.ndarray with 3 dimensions or an iterable containing 2 numpy.ndarrays with 2 dimensions each. Each correspond to the data for one of the input classes, every row corresponds to one example of the data set, every column, one different feature. Returns: Machine: A trained multiclass machine. """


    _trainer = bob.learn.linear.CGLogRegTrainer(**{'lambda':self.regularizer})

    if len(X) == 2: #trains and returns a single logistic regression classifer

      return _trainer.train(add_bias(X[0]), add_bias(X[1]))

    else: #trains and returns a multi-class logistic regression classifier

      # use one-versus-all strategy
      machines = []
      for k in range(len(X)):
        NC_range = list(range(0,k)) + list(range(k+1,len(X)))
        machines.append(_trainer.train(add_bias(numpy.vstack(X[NC_range])),
            add_bias(X[k])))

      return MultiClassMachine(machines)