定义了一个列表，包含了所有导出的函数名称

all = ['gunet_k3_t', 'gunet_k7_t', 'gunet_s5_t', 'gunet_s9_t', 'gunet_ln_t', 'gunet_in_t', 'gunet_nn_t', 'gunet_hsig_t', 'gunet_tanh_t', 'gunet_relu_t', 'gunet_gelu_t', 'gunet_idt_t', 'gunet_sum_t', 'gunet_cat_t', 'gunet_se_t', 'gunet_eca_t', 'gunet_rs_t', 'gunet_d2x_t', 'gunet_w2x_t', 'gunet_nb1_t', 'gunet_nb2_t', 'gunet_nb4_t', 'gunet_nb8_t', 'gunet_nb16_t', 'gunet_nb32_t', 'gunet_nb64_t', 'gunet_nb128_t', 'gunet_0wd_t', 'gunet_cwd_t', 'gunet_nf_t', 'gunet_nw_t', 'gunet_ni_t', 'gunet_nmp_t', 'gunet_t_0', 'gunet_t_1', 'gunet_t_2', 'gunet_t_3', 'gunet_t_4']

定义了多个函数，每个函数的作用是返回一个gUNet模型的实例

每个函数的名称中包含了一些字符串，用来表示不同的模型配置

每个函数的实现中，都调用了gUNet类的构造函数来创建模型实例，并传入了不同的参数

这些参数控制了模型的一些配置，例如卷积核大小、基础维度、深度、卷积层类型、归一化层类型、门控激活函数、融合层类型等

def gunet_k3_t(): return gUNet(kernel_size=3, base_dim=24, depths=[2, 2, 2, 4, 2, 2, 2], conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=nn.Sigmoid, fusion_layer=SKFusion)

def gunet_k7_t(): return gUNet(kernel_size=7, base_dim=24, depths=[2, 2, 2, 4, 2, 2, 2], conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=nn.Sigmoid, fusion_layer=SKFusion)

def gunet_s5_t(): return gUNet(kernel_size=5, base_dim=24, depths=[2, 2, 4, 2, 2], conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=nn.Sigmoid, fusion_layer=SKFusion)

def gunet_s9_t(): return gUNet(kernel_size=5, base_dim=24, depths=[2, 2, 2, 2, 4, 2, 2, 2, 2], conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=nn.Sigmoid, fusion_layer=SKFusion)

def gunet_ln_t(): return gUNet(kernel_size=5, base_dim=24, depths=[2, 2, 2, 4, 2, 2, 2], conv_layer=ConvLayer, norm_layer=LayerNorm, gate_act=nn.Sigmoid, fusion_layer=SKFusion)

def gunet_in_t(): return gUNet(kernel_size=5, base_dim=24, depths=[2, 2, 2, 4, 2, 2, 2], conv_layer=ConvLayer, norm_layer=nn.InstanceNorm2d, gate_act=nn.Sigmoid, fusion_layer=SKFusion)

def gunet_nn_t(): return gUNet(kernel_size=5, base_dim=24, depths=[2, 2, 2, 4, 2, 2, 2], conv_layer=ConvLayer, norm_layer=nn.Identity, gate_act=nn.Sigmoid, fusion_layer=SKFusion)

def gunet_hsig_t(): return gUNet(kernel_size=5, base_dim=24, depths=[2, 2, 2, 4, 2, 2, 2], conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=nn.Hardsigmoid, fusion_layer=SKFusion)

def gunet_tanh_t(): return gUNet(kernel_size=5, base_dim=24, depths=[2, 2, 2, 4, 2, 2, 2], conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=nn.Tanh, fusion_layer=SK