Hi @AlexisW!
I faced a similar kind of problem. In my case I had to go through my self-implemented initialization function, that essentially assigns random values to several hyperparameters. The key culprit was in value types. I had to transform all instances of numpy variables to original Python types by explicit casts.
Concrete example would be changing
bidirectional = numpy.random.randint(0,1)

to
bidirectional = bool(numpy.random.randint(0,1))

While this might not be directly the remedy to your situation, I highly recommend pasting the error message and trying to line up the expected parameters with provided parameters. That I can also say already that the first set of expected types has to do with PackedSequence inputs, so I suspect the latter would be the one you should be looking at.
Actually, let me do that for you right now:
(Tensor input,  tuple of Tensors hx,    tuple of Tensors params, 
(Tensor,        Tensor,                 tuple,                  

bool has_biases,    int num_layers, float dropout,  
list,               bool,           int,            

bool train, bool bidirectional, bool batch_first)
float,      bool,               int)

Its much more easier to spot where the discrepancies might lie in terms of expected and provided parameter types. While native types should be casted to corresponding types on-the-fly, that has_biases seems not to work. I’d suggest you check out how you’re initializing your model at the moment.
Cheers,
PN