def METHOD_NAME(expr, vars, pars, name=None, hessian=False):
    r"""
    Create a potential class from an expression for the potential.
    .. note::
        This utility requires having `Sympy <http://www.sympy.org/>`_ installed.
    .. warning::
        These potentials are *not* pickle-able and cannot be written
        out to YAML files (using `~gala.potential.PotentialBase.save()`)
    Parameters
    ----------
    expr : :class:`sympy.core.expr.Expr`, str
        Either a ``Sympy`` expression, or a string that can be converted to
        a ``Sympy`` expression.
    vars : iterable
        An iterable of variable names in the expression.
    pars : iterable
        An iterable of parameter names in the expression.
    name : str (optional)
        The name of the potential class returned.
    hessian : bool (optional)
        Generate a function to compute the Hessian.
    Returns
    -------
    CustomPotential : `~gala.potential.PotentialBase`
        A potential class that represents the input equation. To instantiate the
        potential, use just like a normal class with parameters.
    Examples
    --------
    Here we'll create a potential class for the harmonic oscillator
    potential, :math:`\Phi(x) = \frac{1}{2}\,k\,x^2`:
        >>> Potential = from_equation("1/2*k*x**2", vars="x", pars="k",
        ...                           name='HarmonicOscillator')
        >>> p1 = Potential(k=1.)
        >>> p1
        <HarmonicOscillatorPotential: k=1.00 (dimensionless)>
    The potential class (and object) is a fully-fledged subclass of
    `~gala.potential.PotentialBase` and therefore has many useful methods.
    For example, to integrate an orbit:
        >>> from gala.potential import Hamiltonian
        >>> H = Hamiltonian(p1)
        >>> orbit = H.integrate_orbit([1., 0], dt=0.01, n_steps=1000)
    """
    try:
        import sympy
        from sympy.utilities.lambdify import lambdify
    except ImportError:
        raise ImportError("sympy is required to use 'from_equation()' "
                          "potential class creation.")
    # convert all input to Sympy objects
    expr = sympy.sympify(expr)
    vars = [sympy.sympify(v) for v in vars]
    var_names = [v.name for v in vars]
    pars = [sympy.sympify(p) for p in pars]
    par_names = [p.name for p in pars]
    ndim = len(vars)
    # Energy / value
    energyfunc = lambdify(vars + pars, expr, dummify=False,
                          modules=['numpy', 'sympy'])
    # Gradient
    gradfuncs = []
    for var in vars:
        gradfuncs.append(lambdify(vars + pars, sympy.diff(expr, var),
                                  dummify=False,
                                  modules=['numpy', 'sympy']))
    parameters = {}
    for _name in par_names:
        parameters[_name] = PotentialParameter(_name,
                                               physical_type='dimensionless')
    class CustomPotential(PotentialBase, parameters=parameters):
        ndim = len(vars)
        def _energy(self, w, t=0.):
            kw = self.parameters.copy()
            for k, v in kw.items():
                kw[k] = v.value
            for i, name in enumerate(var_names):
                kw[name] = w[:, i]
            return np.array(energyfunc(**kw))
        def _gradient(self, w, t=0.):
            kw = self.parameters.copy()
            for k, v in kw.items():
                kw[k] = v.value
            for i, name in enumerate(var_names):
                kw[name] = w[:, i]
            grad = np.vstack([f(**kw)[np.newaxis] for f in gradfuncs])
            return grad.T
    if name is not None:
        # name = _classnamify(name)
        if "potential" not in name.lower():
            name = name + "Potential"
        CustomPotential.__name__ = str(name)
    # Hessian
    if hessian:
        hessfuncs = []
        for var1 in vars:
            for var2 in vars:
                hessfuncs.append(lambdify(vars + pars,
                                          sympy.diff(expr, var1, var2),
                                          dummify=False,
                                          modules=['numpy', 'sympy']))
        def _hessian(self, w, t):
            kw = self.parameters.copy()
            for k, v in kw.items():
                kw[k] = v.value
            for i, name in enumerate(var_names):
                kw[name] = w[:, i]
            # expand = [np.newaxis] * w[i].ndim
            # This ain't pretty, bub
            arrs = []
            for f in hessfuncs:
                hess_arr = np.array(f(**kw))
                if hess_arr.shape != w[:, i].shape:
                    hess_arr = np.tile(hess_arr, reps=w[:, i].shape)
                arrs.append(hess_arr)
            hess = np.vstack(arrs)
            return hess.reshape((ndim, ndim, len(w[:, i])))
        CustomPotential._hessian = _hessian
    CustomPotential.save = None
    return CustomPotential