Ah. Okay. How do we implement that? Something like the following?
Custom module.
class CustomModule(nn.Module):
    def __init__(self, input_channels_to_this_block, output_channels_from_this_block):
        super(CustomModule, self).__init__()

        self.cnn2 = nn.Conv2d()
        self.cnn3 = nn.Conv2d()
        self.cnn4 = nn.Conv2d()



    def forward(self, img_rep):
        x = cnn2(img_rep)
        y = cnn3(img_rep)
        z = cnn4(img_rep)

        # perform custom operation on x, y, and z and merge them into a single tensor called p. p is the same shape as x, y, and z.

        o = torch.concat(x, y, z) # or average the 3 tensors so that we have a new tensor that is the same shape as x,y, and z.
        
        return o

Classifier which uses the custom module.
class SomeClassifier(nn.Module):
    def __init__(self):
        super(SomeClassifier, self).__init__()

        # all three conv blocks maintain the original image size.
        self.cnn1 = nn.Conv2d()
        self.custom_block = CustomModule(input_channels_to_this_block, output_channels_from_this_block)
        self.fc = nn.Linear()

    def forward(self, img):
        img_rep = self.cnn1(img)
        p = self.custom_block(img)
        p = torch.flatten()
        p = self.fc(p)
        
        return p