import torch.nn as nn

class ModelA(nn.Module):
    def __init__(self,n_channels):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        #DO ANYTHING HERE
        return x 

class ModelB(nn.Module):
    def __init__(self,n_channels1,n_channels2,n_channels3):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x,y,z):
        #DO ANYTHING HERE
#         x = self.pool(F.relu(self.conv1(x)))
#         x = self.pool(F.relu(self.conv2(x)))
#         x = torch.flatten(x, 1) # flatten all dimensions except batch
#         x = F.relu(self.fc1(x))
#         x = F.relu(self.fc2(x))
#         x = self.fc3(x)
        return x
class MyEnsemble(nn.Module):
    def __init__(self):
        super(MyEnsemble, self).__init__()
        self.modelA = ModelA(1)
        self.modelB = ModelB(1,1,1)
    def forward(self, a, b, c):
        emb = self.modelA(a)
        print("emb",emb)
        output = self.modelB(emb, b, c)
        
        return output
model = MyEnsemble()
model(1, 2, 3)

Sample code