Improve navigation of the I/O how-to by prepending a decision tree/flowchart using an image or graphviz, as suggested by @mattip.

Quick scanning, the following should be corrected:

• "Does Dask have a SQL interface": yes it now does (if perhaps not fully featured)
• paid support: add Coiled? others (saturn)?
• main page Supported By: there was discussion on how to add logos here, but I would say Coiled, Saturn, CapitalOne, Prefect should be here. Also, an older page Funding should be the same with more details, or just

PR #6447 adds a public API to get the maximum number of registers per thread (numba.cuda.Dispatcher.get_regs_per_thread()). There are other attributes that might be nice to provide - shared memory per block, local memory per thread, const memory usage, maximum block size.

These are all available in the FuncAttr named tuple: https://github.com/numba/numba/blob/master/numba/cuda/cudadrv/drive

Is there a way to train a bidirectional RNN (like LSTM or GRU) on trax nowadays?

The current version of the HANS dataset is missing the additional information provided for each example, including the sentence parses, heuristic and subcase.

We may add an equivalent functionality to the NMT evaluation scripts

https://github.com/awslabs/sockeye/blob/3736f18e3286895cb1eb29817396a8b508edd802/sockeye/inference.py#L657-L659

Support Series.median()

MCVE Code Sample

# Your code here
import numpy as np
import xarray as xr

data = np.zeros((10, 4))
example_xr = xr.DataArray(data, coords=[range(10), ["

AutoDelta is used to do MAP inference in Pyro. It would be nice to have it in NumPyro too.

References

• Pyro AutoDelta implementation
• AutoNormal implementatio

🐛 Bug

The napari welcome screen text is extremely low contrast and unreadable.

To Reproduce

Steps to reproduce the behavior:

1. Launch napari & look at the welcome screen

It's very bad on Windows, slightly better on Mac (though even there I can't read it easily at all). This isn't readable.

Screenshot from Windows:
![napari-screenshot](https://user-images.githubusercontent