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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 apply(self, X, ntree_limit=0):
 """Return the predicted leaf every tree for each sample. Parameters X : array_like, shape=[n_samples, n_features] Input features matrix. ntree_limit : int Limit number of trees in the prediction; defaults to 0 (use all trees). Returns ------- X_leaves : array_like, shape=[n_samples, n_trees] For each datapoint x in X and for each tree, return the index of the leaf x ends up in. Leaves are numbered within ``[0; 2**(self.max_depth+1))``, possibly with gaps in the numbering. """

        test_dmatrix = DMatrix(X, missing=self.missing, nthread=self.n_jobs)
        return self.get_booster().predict(test_dmatrix,
                                          pred_leaf=True,
                                          ntree_limit=ntree_limit)