panrep/decoders.py (16 lines):
	- line 39: # TODO consider other formulations metapath2vec
	- line 43: # TODO 2 different type of embeddings context and embedding to improve performance
	- line 235: # TODO Parameterize per node type pairs!!
	- line 357: # TODO implement regularization
	- line 364: # TODO move to dataloader so that it is faster
	- line 392: # TODO filter edges
	- line 424: # TODO implement regularization
	- line 447: # TODO move to dataloader so that it is faster
	- line 507: # TODO move to dataloader so that it is faster
	- line 645: # TODO maybe represent triplets as three arrays to make it faster.
	- line 653: # TODO consider other formulations metapath2vec
	- line 677: # TODO implement regularization
	- line 684: # TODO move to dataloader so that it is faster
	- line 712: # TODO filter edges
	- line 747: # TODO implement regularization
	- line 1128: # TODO summary per node type or across all node types? for infomax


data_handler/imdb/imbd_data_loader.py (13 lines):
	- line 68: # TODO genre to multihot encoding
	- line 133: # TODO Check if this title is important or not.. does it exist in other dictionaries
	- line 152: # TODO decide if these attributes are needed.
	- line 204: # TODO is it an entry per person and movie or list of persons
	- line 210: # TODO Possible Bug. How about when only one or 2 directors or writers exists?
	- line 237: # TODO test data dumping and loading
	- line 307: # TODO do not pass the whole value key only the subsets...
	- line 329: # TODO load first to dictionary and then process in parallel for the nlp model ...
	- line 395: # TODO Check if this title is important or not.. does it exist in other dictionaries
	- line 440: # TODO is it an entry per person and movie or list of persons
	- line 457: # TODO should we include job title and character played ? job category, more meaningfull
	- line 470: # TODO Possible Bug. How about when only one or 2 directors or writers exists?
	- line 497: # TODO test data dumping and loading


data_handler/imdb/imdb_data_loader.py (13 lines):
	- line 68: # TODO genre to multihot encoding
	- line 133: # TODO Check if this title is important or not.. does it exist in other dictionaries
	- line 152: # TODO decide if these attributes are needed.
	- line 204: # TODO is it an entry per person and movie or list of persons
	- line 210: # TODO Possible Bug. How about when only one or 2 directors or writers exists?
	- line 237: # TODO test data dumping and loading
	- line 307: # TODO do not pass the whole value key only the subsets...
	- line 329: # TODO load first to dictionary and then process in parallel for the nlp model ...
	- line 395: # TODO Check if this title is important or not.. does it exist in other dictionaries
	- line 440: # TODO is it an entry per person and movie or list of persons
	- line 457: # TODO should we include job title and character played ? job category, more meaningfull
	- line 470: # TODO Possible Bug. How about when only one or 2 directors or writers exists?
	- line 497: # TODO test data dumping and loading


panrep/classifiers.py (7 lines):
	- line 111: # TODO different layers may have different number of hidden units current implementation prevents
	- line 190: # TODO implement regularization
	- line 271: # TODO maybe represent triplets as three arrays to make it faster.
	- line 276: # TODO consider other formulations metapath2vec
	- line 298: # TODO implement regularization
	- line 305: # TODO move to dataloader so that it is faster
	- line 361: # TODO implement regularization


panrep/load_data.py (7 lines):
	- line 499: #TODO fix this is wrong had to add all edges in the testign graph
	- line 823: # TODO possibly filter out again the frequent nonzero columns
	- line 906: # TODO THIS IS WRONG!!! I have to add the train valid and test
	- line 1705: TODO add metapaths
	- line 1772: TODO add motifs
	- line 1778: TODO add metapaths
	- line 1885: TODO add metapaths


panrep/layers.py (5 lines):
	- line 120: # TODO check that the masking step works
	- line 123: # TODO use sum instead of mean
	- line 125: # TODO check the masked 1 with without mask that returns the same
	- line 456: # TODO implement this layer
	- line 537: # TODO sum for the link prediction to not consider the zero messages


data_handler/imdb/imdb_data_to_graph.py (4 lines):
	- line 19: # TODO how to treat missing attributes
	- line 153: # TODO change to torch
	- line 162: # TODO conver to torch th.array()
	- line 164: # TODO check datatype


panrep/edge_masking_samling.py (4 lines):
	- line 35: #values_o = np.random.randint(num_entity_o, size=num_to_generate) TODO add later
	- line 258: # TODO 1. Make sure the graph is connected (how) ? rgcn does not
	- line 271: # TODO negative samples
	- line 321: # TODO negative samples


panrep/evaluation.py (3 lines):
	- line 25: # TODO have to map the etype and ntype to their integer ids.
	- line 431: # TODO have to map the etype and ntype to their integer ids.
	- line 1021: # TODO find all zero indices rows and remove.


panrep/encoders.py (3 lines):
	- line 22: # TODO different layers may have different number of hidden units current implementation prevents
	- line 73: # TODO possibly high complexity here??
	- line 182: # TODO possibly high complexity here??


panrep/utils.py (3 lines):
	- line 285: # TODO filter positive edges
	- line 293: # TODO (lingfan): implement filtered metrics
	- line 327: # TODO filter positive edges