OK, I think I’ve understood it.
I assume both datasets have the same length.
Here is a small example. I’ve modified your model to work with dummy data:

class hybrid_cnn(nn.Module):
    def __init__(self,**kwargs):
        super(hybrid_cnn,self).__init__()
        resnet = torchvision.models.resnet50(pretrained=False)
        self.base = nn.Sequential(*list(resnet.children())[:-2])
        
        setattr(self,"fc0",nn.Linear(100352, 2))
        setattr(self,"fc1",nn.Linear(100352, 2))


    def forward(self,x):
        x = self.base(x)
        clf_outputs = {}
        num_fcs = 2
        x = x.view(x.size(0), -1)
        xs = torch.cat([x[::2], x[1::2]])
        for i in range(num_fcs):
            clf_outputs["fc%d" %i] = getattr(self, "fc%d" %i)(xs[i])

        return clf_outputs


class MyDatasetA(Dataset):
    def __init__(self):
        self.data = torch.randn(640, 3, 224, 224)
        
    def __getitem__(self, index):
        return self.data[index]
    
    def __len__(self):
        return len(self.data)

    
class MyDatasetB(Dataset):
    def __init__(self):
        self.data = torch.randn(640, 3, 224, 224)
        
    def __getitem__(self, index):
        return self.data[index]
    
    def __len__(self):
        return len(self.data)


class MyDatasetC(Dataset):
    def __init__(self):
        self.datasetA = MyDatasetA()
        self.datasetB = MyDatasetB()
        
    def __getitem__(self, index):
        dataA = self.datasetA[index].unsqueeze(0)
        dataB = self.datasetB[index].unsqueeze(0)
        
        data = torch.cat((dataA, dataB), 0)
        return data
    
    def __len__(self):
        return len(self.datasetA)
    
dataset = MyDatasetC()
x = dataset[0]
x.shape

loader = DataLoader(
    dataset,
    batch_size=64,
    shuffle=True,
    num_workers=1
)

# Your training routine (just one iteration)
loader_iter = iter(loader)
x = loader_iter.next()
x = x.view(-1, 3, 224, 224)
model = hybrid_cnn()
output = model(x)

Let me know, if this works for you.
EDIT: I think the datasets are currently interleaved. Let me check it real quick.
EDIT2: Should work now.