Bildschirmfoto 2020-01-09 um 23.20.031021×562 231 KB

I am trying to implemnt this model. I have the following code:
class MyModelA(nn.Module):
    def __init__(self):
        super(MyModelA, self).__init__()
        self.conv1 = nn.Sequential(
                    nn.Conv1d(in_channels=256, out_channels=252, kernel_size=5),
                    nn.softmax(),
                    nn.Avgpool1d(kernel_size= 2, stride=2),
                    nn.Dropout(p=0.25))  
        self.conv2 = nn.Conv1d(in_channels=126, out_channels=122, kernel_size= 5),
                    nn.softmax(),
                    nn.Avgpool1d(kernel_size= 2, stride=2),
                     nn.Dropout(p=0.25)
        
    def forward(self, x):
        x = self.fc1(x)
        return x
    

    class MyModelB(nn.Module):
    def __init__(self):
        super(MyModelB, self).__init__()
        self.conv1 = nn.Sequential(
                    nn.Conv1d(in_channels=256, out_channels=252, kernel_size=5),
                    nn.softmax(),
                    nn.Avgpool1d(kernel_size= 2, stride=2),
                    nn.Dropout(p=0.25)) 
        self.conv2 = nn.Conv1d(in_channels=126, out_channels=122, kernel_size= 5),
                    nn.softmax(),
                    nn.Avgpool1d(kernel_size= 2, stride=2),
                     nn.Dropout(p=0.25)
        
    def forward(self, x):
        x = self.fc1(x)
        return x
    
     class MyModelC(nn.Module):
        def __init__(self):
        super(MyModelC, self).__init__()
        self.conv1 = nn.Sequential(
                    nn.Conv1d(in_channels=256, out_channels=252, kernel_size=5),
                    nn.softmax(),
                    nn.Avgpool1d(kernel_size= 2, stride=2),
                    nn.Dropout(p=0.25))  
        self.conv2 = nn.Conv1d(in_channels=126, out_channels=122, kernel_size= 5),
                    nn.softmax(),
                    nn.Avgpool1d(kernel_size= 2, stride=2),
                     nn.Dropout(p=0.25)

    class MyEnsemble(nn.Module):
    def __init__(self, modelA, modelB):
        super(MyEnsemble, self).__init__()
        self.modelA = modelA

Now i have to merge this three classes (MyModelA, MyModelB and MyModelC) into one. Do i use
tensor.cat(MyModelA, MyModelB and MyModelC) or is there something else that i should do to merge this 3 models ?
Please note the code is not complete, i am editing still. Thanks to @ptrblck for the model converging cocept code.