Fix off-by-one

            linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))

            # Add an activation func except last layer

            if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 3:

            if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 2:

                pass

            elif activation_func in self.activation_dict:

                linears.append(self.activation_dict[activation_func]())

                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))

            # Add dropout except last layer

            if use_dropout and i < len(layer_structure) - 3:

            if use_dropout and i < len(layer_structure) - 2:

                linears.append(torch.nn.Dropout(p=0.3))

        self.linear = torch.nn.Sequential(*linears)