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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):
 """Predict for X. For dask inputs, a dask array or dataframe is returned. For other inputs (NumPy array, pandas dataframe, scipy sparse matrix), the regular return value is returned. Parameters X : array-like Returns ------- y : array-like """

        self._check_method("predict")
        X = self._check_array(X)

        if isinstance(X, da.Array):
            result = X.map_blocks(
                _predict, dtype="int", estimator=self._postfit_estimator, drop_axis=1
            )
            return result

        elif isinstance(X, dd._Frame):
            return X.map_partitions(
                _predict, estimator=self._postfit_estimator, meta=np.array([1])
            )

        else:
            return _predict(X, estimator=self._postfit_estimator)