Project: s41467-024-44864-5 | Function: forward (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** U-Net deep convolutional neural network forward pass that maps noisy Raman spectra to instrumental noise patterns through encoding-decoding architecture with attention mechanism

**KEY TERMS:** {
  "x": "noisy input spectra",
  "x1-x7": "encoded features at different scales",
  "d1-d7": "decoded features with skip connections",
  "Conv": "convolutional blocks",
  "Maxpool": "downsampling",
  "Up": "upsampling"
}

**CONTEXT CODE:** """
The 1-D U-Net model with channel attention  for instrumental noise modeling
Author: HaoHe
Email: hehao@stu.xmu.edu.cn
Date:2020-09-12
"""
from abc import ABC
import torch
from torch import nn
from CBAM1D import CBAM


class Conv_block(nn.Module, ABC):
    def __init__(self, ch_in, ch_out):
        super(Conv_block, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv1d(ch_in, ch_out, kernel_size=3, stride=1, padding=1, bias=True),
            nn.BatchNorm1d(ch_out),
            nn.ReLU(),
            nn.Conv1d(ch_out, ch_out, kernel_size=3, stride=1, padding=1, bias=True),
            nn.BatchNorm1d(ch_out),
            nn.ReLU()
        )

    def forward(self, x):
        x = self.conv(x)
        return x


class Up_conv(nn.Module, ABC):
    def __init__(self, ch_in, ch_out):
        super(Up_conv, self).__init__()
        self.up = nn.Sequential(
            nn.Upsample(scale_factor=2),
            nn.Conv1d(ch_in, ch_out, kernel_size=3, stride=1, padding=1, bias=True),
            nn.BatchNorm1d(ch_out),
            nn.ReLU()
        )

    def forward(self, x):
        x = self.up(x)
        return x


# Traditional U-net
class UNet(nn.Module, ABC):
    def __init__(self, img_ch=1, output_ch=1):
        super(UNet, self).__init__()

        self.Maxpool = nn.MaxPool1d(kernel_size=2, stride=2)

        self.Conv1 = Conv_block(ch_in=img_ch, ch_out=64)
        self.Conv2 = Conv_block(ch_in=64, ch_out=128)
        self.Conv3 = Conv_block(ch_in=128, ch_out=256)
        self.Conv4 = Conv_block(ch_in=256, ch_out=512)
        self.Conv5 = Conv_block(ch_in=512, ch_out=1024)

        self.Up5 = Up_conv(ch_in=1024, ch_out=512)
        self.Up_conv5 = Conv_block(ch_in=1024, ch_out=512)

        self.Up4 = Up_conv(ch_in=512, ch_out=256)
        self.Up_conv4 = Conv_block(ch_in=512, ch_out=256)

        self.Up3 = Up_conv(ch_in=256, ch_out=128)
        self.Up_conv3 = Conv_block(ch_in=256, ch_out=128)

        self.Up2 = Up_conv(ch_in=128, ch_out=64)
        self.Up_conv2 = Conv_block(ch_in=128, ch_out=64)

        self.Conv_1x1 = nn.Conv1d(64, output_ch, kernel_size=1, stride=1, padding=0)

    def forward(self, x):
        # MASKED: U-Net deep convolutional neural network forward pass that maps noisy Raman spectra to instrumental noise patterns through encoding-decoding architecture with attention mechanism implementation
        raise NotImplementedError("This function is masked for testing purposes.")


# U-Net with channel and spatial attention

class Conv_block_CA(nn.Module, ABC):
    def __init__(self, ch_in, ch_out):
        super(Conv_block_CA, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv1d(ch_in, ch_out, kernel_size=3, stride=1, padding=1, bias=True),
            nn.BatchNorm1d(ch_out),
            nn.ReLU(),
            nn.Conv1d(ch_out, ch_out, kernel_size=3, stride=1, padding=1, bias=True),
            nn.BatchNorm1d(ch_out),
            nn.ReLU()
        )
        self.cbam = CBAM(ch_out)

    def forward(self, x):
        x = self.conv(x)
        x = self.cbam(x)
        return x


class Up_conv_CA(nn.Module, ABC):
    def __init__(self, ch_in, ch_out):
        super(Up_conv_CA, self).__init__()
        self.up = nn.Sequential(
            nn.Upsample(scale_factor=2),
            nn.Conv1d(ch_in, ch_out, kernel_size=3, stride=1, padding=1, bias=True),
            nn.BatchNorm1d(ch_out),
            nn.ReLU()
        )
        self.cbam = CBAM(ch_out)

    def forward(self, x):
        x = self.up(x)
        x = self.cbam(x)
        return x


class UNet_CA(nn.Module, ABC):
    def __init__(self, ch_in=1, ch_out=1):
        super(UNet_CA, self).__init__()
        self.Maxpool = nn.MaxPool1d(kernel_size=2, stride=2)

        self.Conv1 = Conv_block_CA(ch_in=ch_in, ch_out=64)
        self.Conv2 = Conv_block_CA(ch_in=64, ch_out=128)
        self.Conv3 = Conv_block_CA(ch_in=128, ch_out=256)
        self.Conv4 = Conv_block_CA(ch_in=256, ch_out=512)
        self.Conv5 = Conv_block_CA(ch_in=512, ch_out=1024)


Complete the function implementation replacing "raise NotImplementedError".