I am new to deep learning and want to know how to adopt attention mechanism in the following residual encoder decoder CNN model

Question

I am new to deep learning and want to know how to adopt attention mechanism in the following residual encoder decoder CNN model.can any one help me out please. how to insert channel attention models and spatial attention models in encoder and between the encoder and decoder modules #residual encoder decoder CNN model for medical image segmentation

class Generator(nn.Module):

def __init__(self, in_channels=1, out_channels=96, num_layers=10, kernel_size=5, padding=0):

    super(Generator, self).__init__()

    encoder = [

        nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=1, padding=padding)

        #nn.InstanceNorm2d(out_channels, eps=1e-5, momentum=0.01, affine=True)

        

    ]


    decoder = [

        nn.ConvTranspose2d(out_channels, in_channels, kernel_size=kernel_size, stride=1, padding=padding)

    ]

    for _ in range(num_layers):

        encoder.append(

            nn.Conv2d(out_channels, out_channels, kernel_size=kernel_size, stride=1, padding=padding),

            #nn.InstanceNorm2d(out_channels, eps=1e-5, momentum=0.01, affine=True)

        )

        decoder.append(

            nn.ConvTranspose2d(out_channels, out_channels, kernel_size=kernel_size, stride=1, padding=padding)

        )

    self.encoder = nn.ModuleList(encoder)

    self.decoder = nn.ModuleList(decoder)

    self.__init_weights()

def __init_weights(self):

    for m in self.modules():

        classname = m.__class__.__name__

        if classname.find('Conv') != -1:

            m.weight.data.normal_(0, 0.01)

            if hasattr(m.bias, 'data'):

                m.bias.data.fill_(0)

def forward(self, x: torch.Tensor):

    residuals = []

    for block in self.encoder:

        residuals.append(x)

        x = F.relu(block(x), inplace=True)

    

    for residual, block in zip(residuals[::-1], self.decoder[::-1]):

        x = F.relu((block(x) + residual) , inplace=True)
       
    return x