I am getting the following error.

Traceback (most recent call last):
  File "main.py", line 63, in <module>
    question_classifier = QuestionClassifier(corpus.dictionary, embeddings_index, corpus.max_sent_length, args)
  File "/net/if5/wua4nw/wasi/academic/research_with_prof_chang/projects/question_answering/duplicate_question_detection/source/question_classifier.py", line 26, in __init__
    self.embedding = EmbeddingLayer(len(dictionary), args.emsize, args.dropout)
  File "/if5/wua4nw/anaconda3/lib/python3.5/site-packages/torch/nn/modules/module.py", line 255, in __setattr__
    "cannot assign module before Module.__init__() call")
AttributeError: cannot assign module before Module.__init__() call

I have a class as follows.

class QuestionClassifier(nn.Module):

     def __init__(self, dictionary, embeddings_index, max_seq_length, args):
         self.embedding = EmbeddingLayer(len(dictionary), args.emsize, args.dropout)
         self.encoder = EncoderRNN(args.emsize, args.nhid, args.model, args.bidirection, args.nlayers, args.dropout)
         self.drop = nn.Dropout(args.dropout)

So, when I run the following line:

question_classifier = QuestionClassifier(corpus.dictionary, embeddings_index, corpus.max_sent_length, args)

I get the above mentioned error. Here, EmbeddingLayer and EncoderRNN is a class written by me which inherits nn.module like the QuestionClassifier class.

What I am doing wrong here?

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__()