How to use the econml.utilities.transpose function in econml

def _column_feats(self, X, shift):
        """
        Apply Hermite function evaluations of degrees 0..`degree` differentiated `shift` times.

        When applied to the column `X` of shape(n,), the resulting array has shape(n, (degree + 1)).
        """
        assert ndim(X) == 1
        # this will have dimension (d,) + shape(X)
        coeffs = np.identity(self._degree + shift + 1)[:, shift:]
        feats = ((-1) ** shift) * hermeval(X, coeffs) * np.exp(-X * X / 2)
        # send the first dimension to the end
        return transpose(feats)

X = np.empty((shape(T)[0], 0))
        assert shape(T)[0] == shape(X)[0]

        ft_X = self._x_featurizer.fit_transform(X)
        n = shape(T)[0]
        dT = self._dt_featurizer.fit_transform(T)
        W = np.zeros((n, self._d_w))
        # dT should be an n×dₜ×fₜ array (but if T was a vector, or if there is only one feature,
        # dT may be only 2-dimensional)
        # promote dT to 3D if necessary (e.g. if T was a vector)
        if ndim(dT) < 3:
            dT = reshape(dT, (n, 1, shape(dT)[1]))

        # reshape ft_X and dT to allow cross product (result has shape n×dₜ×fₜ×f_x)
        features = reshape(ft_X, (n, 1, 1, -1)) * reshape(dT, shape(dT) + (1,))
        features = transpose(features, [0, 1, 3, 2])  # swap last two dims to match cross_product
        features = reshape(features, (size(T), -1))
        output = self._model_Y.predict(_add_zeros(np.hstack([W, features])))
        return reshape(output, shape(T) + (shape(output)[-1],))