def check_forward(self, x_data):
x = chainer.Variable(x_data)
y = functions.clip(x, self.x_min, self.x_max)
self.assertEqual(y.data.dtype, numpy.float32)
y_expect = self.x.copy()
for i in numpy.ndindex(self.x.shape):
if self.x[i] < self.x_min:
y_expect[i] = self.x_min
elif self.x[i] > self.x_max:
y_expect[i] = self.x_max
gradient_check.assert_allclose(y_expect, y.data)
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