def test_18934_empty_leaky_relu():
arr = np.random.rand(0,2)
arr_grad = np.empty_like(arr)
autograd.mark_variables([arr], [arr_grad])
with autograd.record():
res = npx.leaky_relu(arr)
res.backward()
def forward(self, x):
if self._mode == 'erf':
return npx.leaky_relu(x, act_type='gelu')
elif self._mode == 'tanh':
return 0.5 * x\
* (1.0 + np.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * (x ** 3))))
elif self._mode == 'sigmoid':
return x * npx.sigmoid(1.702 * x)
else:
raise NotImplementedError