Hi everyone, somewhat of a shameless plug here to bump the thread.
I have been looking for assistance in this problem throughout the forum and on google.  I did find this postpost 1, which does seem promising; however, I am still unsure how to implement it in my code. I also found this link 1, which does seem simple and effective.
Given the lack of responses, I think I may not have provided enough code for assistance.  So I would like to update that as well.
First I load the weights from Glove
weights = torch.FloatTensor(glove_model300.vectors)

# import necessary libraries
from torchtext import vocab
import torchtext.vocab as vocab
import torchtext.data as data
from torchtext.data import Field, Dataset, Example

#create dataframe set
class DataFrameDataset(Dataset):
    """Class for using pandas DataFrames as a datasource"""
    def __init__(self, examples, fields, filter_pred=None):
        self.examples = examples.apply(SeriesExample.fromSeries, args=(fields,), axis=1).tolist()
        if filter_pred is not None:
            self.examples = filter(filter_pred, self.examples)
        self.fields = dict(fields)
        # Unpack field tuples
        for n, f in list(self.fields.items()):
            if isinstance(n, tuple):
                self.fields.update(zip(n, f))
                del self.fields[n]

class SeriesExample(Example):
    @classmethod
    def fromSeries(cls, data, fields):
        return cls.fromdict(data.to_dict(), fields)

    @classmethod
    def fromdict(cls, data, fields):
        ex = cls()
        for key, field in fields.items():
            if key not in data:
                raise ValueError("Specified key {} was not found in "
                "the input data".format(key))
            if field is not None:
                setattr(ex, key, field.preprocess(data[key]))
            else:
                setattr(ex, key, data[key])
                
        return ex

class TextMultiLabelDataset(Dataset):
    def __init__(self, df, tt_text_field, tt_label_field, txt_col, lbl_cols, **kwargs):
        # torchtext Field objects
        fields = [('Word', tt_text_field)]
        for l in lbl_cols: fields.append((l, tt_label_field))
        print(fields)
            
        is_test = False if lbl_cols[0] in df.columns else True
        n_labels = len(lbl_cols)
        
        
        examples = []

#Code found and forked from https://gist.github.com/ohmeow/5b3543a5115040001fce59a105ac4269
class TextMultiLabelDataset(Dataset):
    '''Data Set Class for Multilabel classification'''
    def __init__(self, df, tt_text_field, tt_label_field, txt_col, lbl_cols, **kwargs):
        # torchtext Field objects
        #Extracting the Text in the torch module
        fields = [('text', tt_text_field)]
        #Extracting all the labels 
        for l in lbl_cols: fields.append(('label', tt_label_field))
            
        is_test = False if lbl_cols[0] in df.columns else True
        n_labels = len(lbl_cols)
        
        examples = []
        #Iterating through all the labels
        for idx, row in df.iterrows():
            if not is_test:
                lbls = [ row[l] for l in lbl_cols ]
            else:
                lbls = [0.0] * n_labels
                
            txt = str(row[txt_col])
            examples.append(data.Example.fromlist([txt]+lbls, fields))
                            
        super().__init__(examples, fields, **kwargs)

    @staticmethod
    def sort_key(example): 
        return len(example.text)
    
    @classmethod
    def splits(cls, text_field, label_field, train_df, txt_col, lbl_cols, val_df=None, test_df=None, **kwargs):
        # build train, val, and test data
        train_data, val_data, test_data = (None, None, None)
        
        if train_df is not None: 
            train_data = cls(train_df.copy(), text_field, label_field, txt_col, lbl_cols, **kwargs)
        if val_df is not None: 
            val_data = cls(val_df.copy(), text_field, label_field, txt_col, lbl_cols, **kwargs)
        if test_df is not None: 
            test_data = cls(test_df.copy(), text_field, label_field, txt_col, lbl_cols, **kwargs)

        return tuple(d for d in (train_data, val_data, test_data) if d is not None)

#build multilabel dataset from variable "phon_dup"
#Loading the Dataset
X_train_dataset = TextMultiLabelDataset(phon_dup,TEXT,LABEL, ['Word'], ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12','13', '14'])
#load pretrained embeddings to build text vocabulary
embedding = nn.Embedding.from_pretrained(weights)
#Building the Train Dataset with the Vocab
#numericalizes each word
TEXT.build_vocab(X_train_dataset)

#Marking the Lables with the Vocab
LABEL.build_vocab(X_train_dataset)

# DataIterator for iterating through the Dataset that we have created.
dataset_iter = data.Iterator(X_train_dataset, 1)

for batch in dataset_iter:
    embedding(batch.text)

After this block of newly added code I run the code initially provided.
To ask my question again, with hopes of creating a more clear picture of the advice/help I am seeking.
The code I have provided creates a functional recurrent neural network.  It has input of word embeddings and an output of number strings, where each number in the tensor reflects a letter needed to spell the word coincided for each word embedding.
What I would now like to add is what is outline in blue in the figure above.  That is, an idewntical hidden/recurrent layer structure as to the current model, but with an identical input/output of the tensor “dataset_iter.”  This is reflected in the figure as Arpabet in <->Hidden<-> Recurrent 2 <-> arpabet out.
The second portion of the model that I would like to add is a single hidden layer that connects Arpabet in to Embeddings.
I am fully aware that this model achieves tasks that don’t at first seem relevant to a crowd doing computer vision (awesome!), or other really advanced NN modeling.  However, my goal in the production of this model, is to inform a line of clinical research that may aid in improving treatment/assessment of stroke survivors.
I hope this catches someones eye and I am happy to provide more details/code if I have not provided enough.