Hi @timchen
The module content is in the __init__.py:  https://github.com/pgmmpk/tfrecord/blob/master/tfrecord/__init__.py
I do not have a DataLoader solution yet. That gonna be hard for two reasons:

PyTorch DataLoader is using multiple workers
PyTorch code is not directly usable because TF dataset does not have __len__ (size is indefinite)

But, for a simple "read and convert to torch.Tensor" loop, the answer is very simple - the unit test shows how to get arrays from TFRecord files. What is left is to just wrap them into appropriate torch.Tensor (FloatTensor or IntTensor, depending on what is in the files).
To summarize: its easy to read it into torch.Tensor. But it is hard (for me) to do it very efficiently because of items above.
When I have some results on TFRecordDatasetLoader (and benchmarks), I will share on GitHub.