In order to make the problem more comprehensible, I changed from ModuleList to a simple layer, thus the code has the next form:
want to keep the pre-trained weights intact and add n randomly initialized ones.
In said question, they gave a code snippet that I modified to my problem. It looks like this:
def add_units(self, n_new):
    '''
    n_new : integer variable counting the neurons you want to add
    '''

    # take a copy of the current weights stored in self._fc
    current = self._fc.weight.data
    current_bias = self._fc.bias.data #Only used at the end of the post

    # randomly initialize a tensor with the size of the wanted layer 
    hl_input = torch.zeros([n_new, current.shape[1]])
    nn.init.xavier_uniform_(hl_input, gain=nn.init.calculate_gain('relu'))

    # concatenate the old weights with the new weights
    new_wi = torch.cat([current, hl_input], dim=0)

    # reset weight and grad variables to new size
    self._fc = nn.Linear(current.shape[1]+n_new, 2) #2 is the size of my output layer

    # set the weight data to new values
    self._fc.weight = torch.nn.Parameter(new_wi)

My question is, by doing this am I also copying the bias or only the weights? If I try to do a:
current_bias  = self._fc.bias.data

the dimension that I get equals the output dimension, not the input dimension. What changes should I make to correctly add the old biases into this new layer?
self._fc.bias = current_bias #Does not throw an error but doesn't look right.

Diving into the nn.Linear documentation I found this:
'''
Attributes:
        weight: the learnable weights of the module of shape
            :math:`(\text{out\_features}, \text{in\_features})`. The values are
            initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
            :math:`k = \frac{1}{\text{in\_features}}`
        bias:   the learnable bias of the module of shape :math:`(\text{out\_features})`.
                If :attr:`bias` is ``True``, the values are initialized from
                :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
                :math:`k = \frac{1}{\text{in\_features}}`
'''

So it does make sense that the size equals my output size but I’m still left unsure as to whether my method updates the bias correctly or not.