conv-emotion | repo contains implementation of different architectures | Natural Language Processing library

I am trying to implement the preprocessing code for this paper (code in this repo). The preprocessing code is described in the paper here:

"A convolutional neural network (Kim, 2014) is used to extract textual features from the transcript of the utterances. We use a single convolutional layer followed by max-pooling and a fully connected layer to obtain the feature representations for the utterances. The input to this network is the 300 dimensional pretrained 840B GloVe vectors (Pennington et al., 2014). We use filters of size 3, 4 and 5 with 50 feature maps in each. The convoluted features are then max-pooled with a window size of 2 followed by the ReLU activation (Nair and Hinton, 2010). These are then concatenated and fed to a 100 dimensional fully connected layer, whose activations form the representation of the utterance. This network is trained at utterance level with the emotion labels."

The authors of the paper state that CNN feature extraction code can be found in this repo. However, this code is for a complete model that does sequence classification. It does everything in the quote above except the bolded part (and it goes further to complete do classification). I want the edit the code to build that concatenates and feeds into the 100d layer and then extracts the activations. The data to train on is found in the repo (its the IMDB dataset).

The output should be a (100, ) tensor for each sequence.

Here's the code for the CNN model: