I have a code that used sequence layers. It performed the Unet network. I want to get the intermediate output likes the figure (1x1 conv+sigmoid). How can I get them? This is code

class UnetSkipConnectionBlock(nn.Module):
    def __init__(self, outer_nc, inner_nc, input_nc=None, submodule=None, outermost=False, 
                                innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):
        super(UnetSkipConnectionBlock, self).__init__()
        self.outermost = outermost
        if type(norm_layer) == functools.partial:
            use_bias = norm_layer.func == nn.InstanceNorm2d
        else:
            use_bias = norm_layer == nn.InstanceNorm2d
        if input_nc is None:
            input_nc = outer_nc
        downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, stride=2, padding=1, bias=use_bias)

        if outermost:
            upconv = nn.ConvTranspose2d(inner_nc*2, outer_nc, kernel_size=4, stride=2, padding=1)
            down = [downconv]
            up = [nn.ReLU(True), upconv, nn.Tanh()]
            model = down + [submodule] + up
        elif innermost:
            upconv = nn.ConvTranspose2d(inner_nc, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias)
            down = [nn.LeakyReLU(0.2, True), downconv]
            up = [nn.ReLU(True), upconv, norm_layer(outer_nc)]
            model = down + up
        else:
            upconv = nn.ConvTranspose2d(inner_nc*2, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias)
            down = [nn.LeakyReLU(0.2, True), downconv, norm_layer(inner_nc)]
            up = [nn.ReLU(True), upconv, norm_layer(outer_nc)]

            if use_dropout:
                model = down + [submodule] + up + [nn.Dropout(0.5)]
            else:
                model = down + [submodule] + up

        self.model = nn.Sequential(*model)

    def forward(self, x):
        if self.outermost:
            return self.model(x)
        else:
            return torch.cat([x, self.model(x)], 1)

class UnetGenerator(nn.Module):
    def __init__(self, input_nc, output_nc, num_downs, ngf=64,
                 norm_layer=nn.BatchNorm2d, use_dropout=False):
        super(UnetGenerator, self).__init__()

        unet_block = UnetSkipConnectionBlock(ngf*8, ngf*8, submodule=None, norm_layer=norm_layer, innermost=True)
        for i in range(num_downs - 5):
            unet_block = UnetSkipConnectionBlock(ngf*8, ngf*8, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout)
        unet_block = UnetSkipConnectionBlock(ngf*4, ngf*8, submodule=unet_block, norm_layer=norm_layer)
        unet_block = UnetSkipConnectionBlock(ngf*2, ngf*4, submodule=unet_block, norm_layer=norm_layer)
        unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, submodule=unet_block, norm_layer=norm_layer)
        unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer)
        self.unet_model = unet_block

    def forward(self, input):
        return self.unet_model(input)