torchbiggraph/rpc.py (3 lines):
	- line 17: # FIXME: is it efficient to torch.save into a buf? It's going to have to copy
	- line 23: # FIXME: how to properly copy bytes to ByteTensor?
	- line 44: # FIXME: we've got to get rid of this two-pass nonsense for dynamically sized


torchbiggraph/config.py (3 lines):
	- line 501: # TODO Check that all partitioned entity types have the same number of
	- line 503: # TODO Check that the batch size is a multiple of the batch negative number
	- line 518: # TODO make this a non-inplace operation


torchbiggraph/losses.py (3 lines):
	- line 37: # FIXME: This is not ideal. Perhaps we should pass in the config
	- line 101: # FIXME Workaround for https://github.com/pytorch/pytorch/issues/15223.
	- line 139: # FIXME Workaround for https://github.com/pytorch/pytorch/issues/15870


torchbiggraph/bucket_scheduling.py (3 lines):
	- line 127: # TODO Change this function to use the same cost model as the LockServer
	- line 434: # TODO If init_tree is enabled, during the first pass we might want to
	- line 469: # TODO It may be interesting to get a random bucket among the acquirable


torchbiggraph/train_cpu.py (2 lines):
	- line 727: # FIXME join distributed workers (not really necessary)
	- line 855: # FIXME should we only delay if iteration_idx == 0?


torchbiggraph/schema.py (2 lines):
	- line 21: # TODO Remove noqa when flake8 will understand kw_only added in attrs-18.2.0.
	- line 211: # TODO Remove noqa when flake8 will understand kw_only added in attrs-18.2.0.


torchbiggraph/util.cpp (2 lines):
	- line 246: * the left- and right-hand side of relation type i. (TODO: remove these
	- line 317: // TODO check type and sizes first;


torchbiggraph/operators.py (2 lines):
	- line 121: # FIXME This adapts from the pre-D14024710 format; remove eventually.
	- line 300: # FIXME This adapts from the pre-D14024710 format; remove eventually.


torchbiggraph/batching.py (2 lines):
	- line 28: # FIXME Is PyTorch's sort stable? Won't this risk messing up the random shuffle?
	- line 109: # FIXME: it's not really safe to do partial batches if num_batch_negs != 0


torchbiggraph/parameter_sharing.py (2 lines):
	- line 68: FIXME: torchbiggraph.rpc should be fixed to not require torch.serialization,
	- line 70: FIXME: torch.distributed.recv should not require you to provide the


torchbiggraph/entitylist.py (1 line):
	- line 67: # TODO We could check that, for all i, we have either tensor[i] < 0 or


torchbiggraph/checkpoint_storage.py (1 line):
	- line 293: # FIXME: there's a slight danger here, say that a multi-machine job fails


torchbiggraph/train_gpu.py (1 line):
	- line 232: # TODO have two permanent storages on GPU and move stuff in and out


torchbiggraph/tensorlist.py (1 line):
	- line 137: # FIXME: this is a terrible API


torchbiggraph/util.py (1 line):
	- line 287: # FIXME Add the rank to the name of each process.


torchbiggraph/eval.py (1 line):
	- line 148: # FIXME This order assumes higher affinity on the left-hand side, as it's