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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 predict(self, X, b=0.5, pos_label=1, return_probs=False):
 """Return numpy array of class predictions for X based on predicted marginal probabilities. :param X: Input data. :param b: Decision boundary *for binary setting only*. :type b: float :param pos_label: Positive class index *for binary setting only*. Default: 1 :type pos_label: int :param return_probs: If True, return predict probability. Default: False :type return_probs: bool """


        if self._check_input(X):
            X = self._preprocess_data(X)

        Y_prob = self.marginals(X)

        if self.cardinality > 2:
            Y_pred = Y_prob.argmax(axis=1) + 1
            if return_probs:
                return Y_pred, Y_prob
            else:
                return Y_pred

        if pos_label not in [1, 2]:
            raise ValueError("pos_label must have values in {1,2}.")
        self.logger.info(f"Using positive label class {pos_label} with threshold {b}")

        Y_pred = np.array(
            [pos_label if p[pos_label - 1] > b else 3 - pos_label for p in Y_prob]
        )
        if return_probs:
            return Y_pred, Y_prob
        else:
            return Y_pred