Based on your image, it looks like your model has two different feature extractors, which are concatenated and passed into another module.
Depending on your model definition, you could just change the forward pass and return the features from the first feature extractor.
I’ve created a simple example. The commented code would call the complete model.
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.feature1 = nn.Sequential(
            nn.Linear(100, 20),
            nn.ReLU(),
            nn.Linear(20, 5)
        )
        self.feature2 = nn.Sequential(
            nn.Linear(100, 20),
            nn.ReLU(),
            nn.Linear(20, 5)
        )
        
        self.main_block = nn.Sequential(
            nn.Linear(10, 5),
            nn.ReLU(),
            nn.Linear(5, 2),
            nn.LogSoftmax(dim=1)
        )
        
    def forward(self, x):
        x1 = self.feature1(x)
        x2 = self.feature2(x)
        
        #x = torch.cat((x1, x2), dim=1)
        #x = self.main_block(x)
        return x1 #x

model = MyModel()
x = torch.randn(1, 100)        
output = model(x)

If it’s not possible to change the forward pass for some reason, you could try to register a forward hook on the specific layer. Here 15 is a small example.