from the stack-trace it looks like your problem is not stack.
You are sending 3D inputs to Conv2d which wants 4D inputs (mini-batch x channels x height x width).
You need to use Conv1d for your purposes. I’ve modified the definition that’ll work for you:
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()

        self.elu = nn.ELU(alpha = 1.0, inplace = False)

        self.conv1 = nn.Conv1d(in_channels = 3,
                               out_channels = 8,
                               kernel_size = 1,
                               padding = 0)

        self.conv2 = nn.Conv1d(in_channels = 8,
                               out_channels = 12,
                               kernel_size = 3,
                               padding = 0)

        self.fc1 = nn.Linear(96, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 2)

    def forward(self, x):

        x = tc.stack([tc.atan(x),tc.exp(x),x], dim = 1)
        x = self.elu(self.conv1(x))
        x = self.elu(self.conv2(x))
        x = x.view(-1, 96)
        x = self.elu(self.fc1(x))
        x = self.elu(self.fc2(x))
        x = self.fc3(x)
        return x