human
Solve the following python programming problem given the following code:
def check_grad(layer, x0, seed = 1, eps = None, rtol = None, atol = None) : 
    '
    Numerical gradient checking of layer bprop.
    ' 
    def fun(x) : 
        ca.random.seed (seed) 
        y = np.array (layer.fprop (ca.array (x))).astype (np.float_) 
        return np.sum (y) 
def fun_grad(x) : 
        y = layer.fprop (ca.array (x)) 
        y_grad = ca.ones_like (y, dtype = ca.float_) 
        x_grad = np.array (layer.bprop (y_grad)) 
        return x_grad 
g_approx = approx_fprime (x0, fun, seed) 
    g_true = fun_grad (x0) 
    assert gradclose (g_true, g_approx, rtol, atol) 
    if isinstance (layer, ParamMixin) : 
        def fun(x, p_idx) : 
            ca.random.seed (seed) 
            param_array = layer.params [p_idx].array 
            param_array *= 0 
            param_array += ca.array (x) 
            y = np.array (layer.fprop (ca.array (x0))).astype (np.float_) 
            return np.sum (y) 
def fun_grad(x, p_idx) : 
            param_array = layer.params [p_idx].array 
            param_array *= 0 
            param_array += ca.array (x) 
            out = layer.fprop (ca.array (x0)) 
            y_grad = ca.ones_like (out, dtype = ca.float_) 
            layer.bprop (y_grad) 
            param_grad = np.array (layer.params [p_idx].grad ()) 
            return param_grad.astype (np.float_) 
for (p_idx, p) in enumerate (layer.params) : 
            x = np.array (layer.params [p_idx].array) 
            g_true = fun_grad (x, p_idx) 
            g_approx = approx_fprime (x, fun, eps, p_idx) 
            assert gradclose (g_true, g_approx, rtol, atol) 

    
    

    Fix the buggy line: g_approx = approx_fprime (x0, fun, seed)
assistant
#Here is Python code to fix the buggy line: g_approx = approx_fprime (x0, fun, seed)
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g_approx = approx_fprime (x0, fun, eps)