# Buggy code
def relu_deriv(a_sh):
    """
    Compute the derivative of Relu

    Args:
        a_sh (AdditiveSharingTensor): the private tensor on which the op applies

    Returns:
        0 if Dec(a_sh) < 0
        1 if Dec(a_sh) > 0
        encrypted in an AdditiveSharingTensor
    """
    assert a_sh.dtype != "custom", (
        "`custom` dtype shares are unsupported in SecureNN, use dtype = `long` or `int` instead"
    )

    alice, bob = a_sh.locations
    crypto_provider = a_sh.crypto_provider
    L = a_sh.field
    dtype = get_dtype(L)
    # Common randomness
    u = _shares_of_zero(1, L, dtype, crypto_provider, alice, bob)

    # 1)
    y_sh = a_sh * 2

    # 2) Not applicable with algebraic shares
    y_sh = share_convert(y_sh)

    # 3)
    alpha_sh = msb(y_sh)

    # 4)
    j = sy.MultiPointerTensor(
        children=[
            torch.tensor([0]).send(alice, **no_wrap),
            torch.tensor([1]).send(bob, **no_wrap),
        ]
    )
    gamma_sh = j - alpha_sh + u
    return gamma_sh


# Traceback (TypeError)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-7-902fb9b77716> in <module>
12 x = th.tensor([1.1,2.0,3.2,4.0]).fix_precision().share(*workers, crypto_provider=sw, requires_grad=True)
13 m=th.nn.Linear(4, 1).fix_precision().share(*workers, crypto_provider=sw, requires_grad=True)
---> 14 o=m(x)

/usr/local/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
491             result = self._slow_forward(*input, **kwargs)
492         else:
--> 493             result = self.forward(*input, **kwargs)
494         for hook in self._forward_hooks.values():
495             hook_result = hook(self, input, result)

/usr/local/lib/python3.7/site-packages/torch/nn/modules/linear.py in forward(self, input)
90     @weak_script_method
91     def forward(self, input):
---> 92         return F.linear(input, self.weight, self.bias)
93
94     def extra_repr(self):

/usr/local/lib/python3.7/site-packages/syft/frameworks/torch/hook/hook.py in overloaded_func(*args, **kwargs)
745                 handle_func_command = TorchTensor.handle_func_command
746
--> 747             response = handle_func_command(command)
748
749             return response

/usr/local/lib/python3.7/site-packages/syft/frameworks/torch/tensors/interpreters/native.py in handle_func_command(cls, command)
310             new_command = (cmd, None, new_args, new_kwargs)
311             # Send it to the appropriate class and get the response
--> 312             response = new_type.handle_func_command(new_command)
313             # Put back the wrappers where needed
314             response = syft.frameworks.torch.hook_args.hook_response(

/usr/local/lib/python3.7/site-packages/syft/frameworks/torch/tensors/interpreters/autograd.py in handle_func_command(cls, command)
236             # Try to get recursively the attributes in cmd = "<attr1>.<attr2>.<attr3>..."
237             cmd = cls.rgetattr(cls, cmd)
--> 238             return cmd(*args, **kwargs)
239         except AttributeError:
240             pass

/usr/local/lib/python3.7/site-packages/syft/frameworks/torch/tensors/interpreters/autograd.py in linear(*args)
201                     Un-hook the function to have its detailed behaviour
202                     """
--> 203                     return torch.nn.functional.native_linear(*args)
204
205                 module.linear = linear

/usr/local/lib/python3.7/site-packages/torch/nn/functional.py in linear(input, weight, bias)
1406         ret = torch.addmm(bias, input, weight.t())
1407     else:
-> 1408         output = input.matmul(weight.t())
1409         if bias is not None:
1410             output += bias

/usr/local/lib/python3.7/site-packages/syft/frameworks/torch/tensors/interpreters/autograd.py in method_with_grad(*args, **kwargs)
138                 )
139
--> 140                 result = getattr(new_self, name)(*new_args, **new_kwargs)
141
142                 # Put back SyftTensor on the tensors found in the response

/usr/local/lib/python3.7/site-packages/syft/frameworks/torch/tensors/interpreters/precision.py in matmul(self, *args, **kwargs)
414
415         # Send it to the appropriate class and get the response
--> 416         response = getattr(new_self, "matmul")(*new_args, **new_kwargs)
417
418         # Put back SyftTensor on the tensors found in the response

/usr/local/lib/python3.7/site-packages/syft/frameworks/torch/tensors/interpreters/additive_shared.py in matmul(self, other)
515             return self._public_mul(other, "matmul")
516
--> 517         return self._private_mul(other, "matmul")
518
519     def mm(self, *args, **kwargs):

/usr/local/lib/python3.7/site-packages/syft/frameworks/torch/tensors/interpreters/additive_shared.py in _private_mul(self, other, equation)
413             raise AttributeError("For multiplication a crypto_provider must be passed.")
414
--> 415         shares = spdz.spdz_mul(cmd, self, other, self.crypto_provider, self.field)
416
417         return shares

/usr/local/lib/python3.7/site-packages/syft/frameworks/torch/crypto/spdz.py in spdz_mul(cmd, x_sh, y_sh, crypto_provider, field)
43         j = sy.MultiPointerTensor(children=[j1, j0])
44     else:
---> 45         j = sy.MultiPointerTensor(children=[j1] + j0.child.values())
46
47     delta_b = cmd(delta, b)

TypeError: can only concatenate list (not "dict_values") to list

# Fixed code
def relu_deriv(a_sh):
    """
    Compute the derivative of Relu

    Args:
        a_sh (AdditiveSharingTensor): the private tensor on which the op applies

    Returns:
        0 if Dec(a_sh) < 0
        1 if Dec(a_sh) > 0
        encrypted in an AdditiveSharingTensor
    """
    assert a_sh.dtype != "custom", (
        "`custom` dtype shares are unsupported in SecureNN, use dtype = `long` or `int` instead"
    )

    workers = a_sh.locations
    crypto_provider = a_sh.crypto_provider
    L = a_sh.field
    dtype = get_dtype(L)
    # Common randomness
    u = _shares_of_zero(1, L, dtype, crypto_provider, *workers)

    # 1)
    y_sh = a_sh * 2

    # 2) Not applicable with algebraic shares
    y_sh = share_convert(y_sh)

    # 3)
    alpha_sh = msb(y_sh)

    # 4)
    j = sy.MultiPointerTensor(
        children=[
            torch.tensor([int(i == 0)]).send(w, **no_wrap)
            for i, w in enumerate(workers)
        ]
    )
    gamma_sh = j - alpha_sh + u
    return gamma_sh