Can any one help me in a way to adopt attention mechanism in the the following Residual encoder decoder CNN model please

vision
1

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import torch

import torch.nn as nn

import torch.nn.functional as F

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)

    ]

    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)

        )

        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

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