nn.ConvTranspose2d, Upscaling back to original image dimensions?

Just learning the ropes on PyTorch. Kind of a newbie here.
Attempting to construct a convolutional autoencoder on MNIST.
Here is my desired network architecture:

class tCNN_Autoencoder(nn.Module):
    def __init__(self):
        super(tCNN_Autoencoder, self).__init__()
        # Encoder
        self.en_conv1 = nn.Conv2d(1,16,3,padding=1)
        self.en_conv2 = nn.Conv2d(16,32,3,padding=1)
        self.en_conv3 = nn.Conv2d(32,4,3,padding=1)
        # Decoder
        self.de_tconv1 = nn.ConvTranspose2d(4,32,2,stride=2)
        self.de_tconv2 = nn.ConvTranspose2d(32,16,2,stride=2)
        self.de_tconv3 = nn.ConvTranspose2d(16,1,2,stride=2)
        self.pool = nn.MaxPool2d(2,2)
    def forward(self, x):
        x = F.relu(self.en_conv1(x))
        x = self.pool(x)
        x = F.relu(self.en_conv2(x))
        x = self.pool(x)
        x = F.relu(self.en_conv3(x))
        x = self.pool(x) #Compressed representation
        x = F.relu(self.de_tconv1(x))
        x = F.relu(self.de_tconv2(x))
        x = F.sigmoid(self.de_tconv3(x)) # sigmoid to scale the pixel values in grayscale from 0 to 1
        return x

And here are my tensor sizes that I get when I run my training loop:

torch.Size([28, 1, 28, 28]) before conv1
torch.Size([28, 16, 28, 28]) after conv1
torch.Size([28, 16, 14, 14]) after pooling at conv1
torch.Size([28, 32, 14, 14]) after conv2
torch.Size([28, 32, 7, 7]) after pooling at conv2
torch.Size([28, 4, 7, 7]) after conv3
torch.Size([28, 4, 3, 3]) after final pooling (compressed representation)
torch.Size([28, 32, 6, 6]) after tconv1
torch.Size([28, 16, 12, 12]) after tconv2
torch.Size([28, 1, 24, 24]) after sigmoid

I can see that I am losing 4 pixels on each h x w dimension, but I don’t completely understand how to scale back up to the original image size of 28x28 using the tconv layers.

I suppose it would be related to the downsampled output from the final intermediate max pool layer (showing the compressed representation), giving me a 3x3…but I can’t really upscale that nicely into a 28x28 because 3 is not a multiple of 28. Is there a general principle here that I am ignoring?