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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 _rolling_lstsq(x, y):

 """Finds solution for the rolling case. Matrix formulation."""

    if x.ndim == 2:
        # Treat everything as 3d and avoid AxisError on .swapaxes(1, 2) below
        # This means an original input of:
        #     array([0., 1., 2., 3., 4., 5., 6.])
        # becomes:
        # array([[[0.],
        #         [1.],
        #         [2.],
        #         [3.]],
        #
        #        [[1.],
        #         [2.],
        #         ...
        x = x[:, :, None]
    elif x.ndim <= 1:
        raise np.AxisError("x should have ndmi >= 2")
    return np.squeeze(
        np.matmul(
            np.linalg.inv(np.matmul(x.swapaxes(1, 2), x)),
            np.matmul(x.swapaxes(1, 2), np.atleast_3d(y)),
        )
    )