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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 set_post_evaluation_transform(self, value):
 r""" Set the post processing transform applied after the prediction value from the tree ensemble. Parameters value: str A value denoting the transform applied. Possible values are: - "NoTransform" (default). Do not apply a transform. - "Classification_SoftMax". Apply a softmax function to the outcome to produce normalized, non-negative scores that sum to 1. The transformation applied to dimension `i` is equivalent to: .. math:: \frac{e^{x_i}}{\sum_j e^{x_j}} Note: This is the output transformation applied by the XGBoost package with multiclass classification. - "Regression_Logistic". Applies a logistic transform the predicted value, specifically: .. math:: (1 + e^{-v})^{-1} This is the transformation used in binary classification. """

        self.tree_spec.postEvaluationTransform = \
                _TreeEnsemble_pb2.TreeEnsemblePostEvaluationTransform.Value(value)