human
Solve the following python programming problem given the following code:
def check_backward(self, x_data, t_data, y_grad) : 
    x = chainer.Variable (x_data) 
    t = chainer.Variable (t_data) 
    W = self.link.W 
    y = self.link (x, t) 
    y.grad = y_grad 
    y.backward () 
    negative_sampling.NegativeSamplingFunction.samples = y.creator.samples 
    def f() : 
        return (self.link (x, t).data,) 
(gx, gW) = gradient_check.numerical_grad (f, (x.data, W.data), (y.grad,), eps = 0.01) 
    del negative_sampling.NegativeSamplingFunction.samples 
    gradient_check.assert_allclose (cuda.to_cpu (gx), cuda.to_cpu (x.grad), atol = 0.0001) 
    gradient_check.assert_allclose (cuda.to_cpu (gW), cuda.to_cpu (gW.grad), atol = 0.0001) 

    
    

    Fix the buggy line: gradient_check.assert_allclose (cuda.to_cpu (gW), cuda.to_cpu (gW.grad), atol = 0.0001)
assistant
#Here is Python code to fix the buggy line: gradient_check.

gradient_check.assert_allclose (cuda.to_cpu (gW), cuda.to_cpu (W.grad), atol = 0.0001)