nn Conv2d 32 kernel_size bias False nn BatchNorm2d 32 nn ReLU nn Conv2

nn.Conv2d(3, 32, kernel_size=(3, 3), bias=False),                    
                nn.BatchNorm2d(32),
                nn.ReLU(),
                nn.Conv2d(32, 32, kernel_size=(3, 3)),
                nn.BatchNorm2d(32),
                nn.Dropout(0.2),
                nn.ReLU(),    
                nn.MaxPool2d(2, 2),                           
                nn.Conv2d(32, 64, kernel_size=(3, 3)),
                nn.BatchNorm2d(64),
                nn.ReLU(),
                nn.Conv2d(64, 64, kernel_size=(3, 3)),
                nn.BatchNorm2d(64),
                nn.Dropout(0.3),      
                nn.ReLU(),
                nn.MaxPool2d(2, 2),                             
                nn.Conv2d(64, 128, kernel_size=(3, 3)),
                nn.ReLU(),
                nn.Conv2d(128, 128, kernel_size=(3, 3)),
                nn.BatchNorm2d(128),
                nn.Dropout(0.3),
                nn.ReLU(),   
                nn.MaxPool2d(2, 2),
                nn.Conv2d(128, 256, kernel_size=(3, 3)),
                nn.ReLU(),
                nn.Conv2d(256, 256, kernel_size=(3, 3)),
                nn.BatchNorm2d(256),                
                nn.ReLU(),
                nn.MaxPool2d(2, 2),                                          
                nn.Flatten(),
                nn.Linear(6400, 64),   
                nn.ReLU(), 
                nn.Dropout(0.2),                              
                nn.Linear(64, 32),
                nn.ReLU(),               
                nn.Linear(32, 4),
                nn.Softmax())