small fix

            # Add an activation func

            if activation_func == "linear" or activation_func is None:

                pass

            # If ReLU, Skip adding it to the first layer to avoid dying ReLU

            elif activation_func == "relu" and i < 1:

                pass

            elif activation_func in self.activation_dict:

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

            else:

                raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}')

            # Add dropout

            if use_dropout:

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

            # Add layer normalization

            if add_layer_norm:

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

            # Add dropout

            if use_dropout:

                p = 0.5 if 0 <= i <= len(layer_structure) - 3 else 0.2

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

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

        if state_dict is not None:

                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")

                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")

                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")

                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu"])

                    with gr.Row():

                        with gr.Column(scale=3):