AttributeError: cannot assign module before Module.__init__() call

Looking at the pytorch source code for Module, we see in the docstring an example of deriving from Module includes:

 class Model(nn.Module):
        def __init__(self):
            super(Model, self).__init__()
            self.conv1 = nn.Conv2d(1, 20, 5)
            self.conv2 = nn.Conv2d(20, 20, 5)

So you probably want to call Module's init the same way in your derived class:

super(QuestionClassifier, self).__init__()

Pytorch keeps track of the submodules(conv1, conv2) you will write in your custom Module. Under the hood, the graph corresponding to your Model is automatically built.

The nested Modules will be added to an OrderedDict _modules (initialized in nn.Module.__init__) See source(L69)

If nn.Module.__init__ is not called (self._modules would equal to None), when trying to add a Module, it will raise an error (no key can be added to None). See source(L540-544)

Inspired from the doc:

 class CustomModule(nn.Module):
        def __init__(self):
            super(CustomModule, self).__init__() # Initialize self._modules as OrderedDict
            self.conv1 = nn.Conv2d(1, 20, 5)     # Add key conv1 to self._modules
            self.conv2 = nn.Conv2d(20, 20, 5)    # Add key conv2 to self._modules 

This usually happens when super class's init has not been called. In this case one should start their Neural network class with super.__init__() call. The code would look like this:

class QuestionClassifier(nn.Module):
    def __init__(self, dictionary, embeddings_index, max_seq_length, args):
       super().__init__()

This super's init call should be within this class's init code.