The goal of this issue is to replace all instances of whitelist and blacklist in torch/quantization/quantize.py. See issue #41443 for more information.

Picking up from #15731, there are many places in numpy where we do something like:

try:
    something_which_throws_typeError
except TypeError:
    raise ValueError("a clearer explanation of what went wrong")

It would produce a marginally clearer error if we could change these to use traceback chaining. Our two options are either:

1. The inner error provides valuable infor

If you join Dask DataFrame on a categorical column, then the outputted Dask DataFrame column is still category dtype. However, the moment you .compute() the outputted Dask DataFrame, then the column is the wrong dtype, not categorical.

Tested on Dask 2.14.0 and Pandas 1.0.3
This example where the category type looks like a float, so after .compute(), the dtype is float.

import dask.d

As an example,

@numba.cuda.jit
def foo():
    x = numba.cuda.local.array(shape=(2, 0), dtype=numba.int64)

foo()

gives ValueError: array length <= 0.

Currently these lines contain a workaround because of this issue:

https://github.com/numba/numba/blob/3399d618b334c75a7c5c1ab85ec02769fb3b4c3b/numba/cuda/cudadrv/devicearray.py#L432-L455

Well, Gumbel Distribution is magical. Basically, given a sequence of K logits, i.e., "\log a_1, \log a_2, ..., \log a_K" and K independent gumbel random variables, i.e., "g_1, g_2, ..., g_K". We have

\argmax_i \log a_i + g_i ~ Categorical({a_i / sum(a)})

This gives you a very simple way to sampl

Support DataFrame.select_dtypes

What happened:
xr.DataArray([1], coords=[('onecoord', [2])]).sel(onecoord=2).to_dataframe(name='name') raise an exception ValueError: no valid index for a 0-dimensional object

What you expected to happen:

the same behavior as: xr.DataArray([1], coords=[('onecoord', [2])]).to_dataframe(name='name')

Anything else we need to know?:

I see that the array after the select