use ragged tensor by default
Yes I agree. Maybe something that lets specify different format depending on the column ? Especially to better control dtype and shape (and ragged for tf)

Oh and I forgot: this one should also fix the second issue found in #477 for the next release
I think it's better if it's clear whether the returned tensor is ragged or not when the type is set to tensorflow.
Previously it was a tensor (not ragged) if numpy could stack the output (which can change depending on the batch of example you take), which make things difficult to handle, as it may sometimes return a ragged tensor and sometimes not.

Therefore I reverted this behavior to always return a ragged tensor as we used to do.