写一段CNN和LSTM并联后得到的flatten层叠加的网络，其中CNN有3次二维卷积，输入数据的batch_size是4，in_channels=1,每个通道的图像尺寸为(22,751)

好的，下面是一段CNN和LSTM并联后得到的flatten层叠加的网络代码，满足你的要求：

import torch
import torch.nn as nn

class CNN_LSTM_Flatten(nn.Module):
    def __init__(self):
        super(CNN_LSTM_Flatten, self).__init__()
        # 定义CNN部分
        self.cnn = nn.Sequential(
            nn.Conv2d(in_channels=1, out_channels=16, kernel_size=(3, 3), padding=(1, 1)),
            nn.ReLU(),
            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(3, 3), padding=(1, 1)),
            nn.ReLU(),
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(3, 3), padding=(1, 1)),
            nn.ReLU()
        )
        # 定义LSTM部分
        self.lstm = nn.LSTM(input_size=64 * 22, hidden_size=128, num_layers=2, batch_first=True)
        # 定义flatten层
        self.flatten = nn.Flatten()
        # 定义全连接层
        self.fc = nn.Linear(751 * 128, 10)
        
    def forward(self, x):
        # x的shape为(batch_size, in_channels, 22, 751)
        batch_size = x.shape[0]
        x = self.cnn(x)  # 经过CNN后的shape为(batch_size, 64, 22, 751)
        x = x.view(batch_size, -1, 64 * 22)  # reshape成(batch_size, 751, 64 * 22)
        x, _ = self.lstm(x)  # 经过LSTM后的shape为(batch_size, 751, 128)
        x = self.flatten(x)  # flatten成(batch_size, 751 * 128)
        x = self.fc(x)  # 经过全连接层后的shape为(batch_size, 10)
        return x

这段代码在`CNN_LSTM`的基础上增加了一个`flatten`层和一个全连接层`fc`。在前向传播过程中，先将输入数据经过CNN和LSTM，再经过flatten层，将(batch_size, 751, 128)的张量flatten成(batch_size, 751 * 128)的形状。最后经过全连接层得到(batch_size, 10)的张量。