I am trying to specify a dynamic amount of layers, which I seem to be doing wrong.
My issue is that when I define the 100 layers here, I will get an error in the forward step.
But when I define the layer properly it works?
Below simplified example

```py
class PredictFromEmbeddParaSmall(LightningModule):
    def __init__(self, hyperparams={'lr': 0.0001}):
        super(PredictFromEmbeddParaSmall, self).__init__()
        #Input is something like tensor.size=[768*100]
        self.TO_ILLUSTRATE = nn.Linear(768, 5)
        self.enc_ref=[]
        for i in range(100):
            self.enc_red.append(nn.Linear(768, 5))
        # gather the layers output sth
        self.dense_simple1 = nn.Linear(5*100, 2)
        self.output = nn.Sigmoid()
    def forward(self, x):
        # first input to enc_red
        x_vecs = []
        for i in range(self.para_count):
            layer = self.enc_red[i]
            # The first dim is the batch size here, output is correct
            processed_slice = x[:, i * 768:(i + 1) * 768]
            # This works and give the out of size 5
            rand = self.TO_ILLUSTRATE(processed_slice)
            #This will fail? Error below
            ret = layer(processed_slice)
            #more things happening we can ignore right now since we fail earlier

```

I get this error when executing "ret = layer.forward(processed\_slice)"

> 
> RuntimeError: Expected object of device type cuda but got device type
> cpu for argument #1 'self' in call to \_th\_addmm
> 
> 
> 

Is there a smarter way to program this? OR solve the error?