Use the provided code snippet to check the activation shape and change the in_features of fc1.
For a CIFAR input of [batch_size, 3, 32, 32], fc1 should have 512 in_features:
class CONV_net(torch.nn.Module):
    def __init__(self, input_channels = 3, output_size = 10):
        super().__init__()
        self.conv1 = torch.nn.Sequential(
        torch.nn.Conv2d(input_channels,16,3,2,1),
        torch.nn.MaxPool2d(2),
        torch.nn.ReLU(),
        torch.nn.BatchNorm2d(16)
        )
        self.conv2 = torch.nn.Sequential(
        torch.nn.Conv2d(16,32,3,1,1),
        torch.nn.MaxPool2d(2),
        torch.nn.ReLU(),
        torch.nn.BatchNorm2d(32)
        )
        self.fc1 = torch.nn.Sequential(
        torch.nn.Linear(512,60),
        torch.nn.Dropout(0.5),
        torch.nn.ReLU()
        )
        self.fc2 = torch.nn.Sequential(
        torch.nn.Linear(60,20),
        torch.nn.Dropout(0.5),
        torch.nn.ReLU()
        )
        self.fc3 = torch.nn.Linear(20, output_size)
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        return x
    
model = CONV_net()
x = torch.randn(2, 3, 32, 32)
out = model(x)