tf.function makes invalid assumptions about arguments that are Mapping instances. In general, there are no requirements for Mapping instances to have constructors that accept [(key, value)] initializers, as assumed here.

This leads to cryptic exceptions when used with perfectly valid Mapping s

There are some interesting algorithms in simulation from Physics, Chemistry, and Engineering especially regarding Monte Carlo simulation: Heat Bath algorithm, Metro-Police algorithm, Markov Chain Monte Carlo, etc.

It says in the documentation (the last section - "Working with Virtual Environments"):

For Python 3 add the following lines to the top of your .wsgi file:

activate_this = '/path/to/env/bin/activate_this.py'
with open(activate_this) as file_:
    exec(file_.read(), dict(__file__=activate_this))

However `activate_this.p

As indicated in the FAQ, the way to import ResNeXt101 is from keras.applications.resnext import ResNeXt101. But in the latest Keras 2.3.1 and Keras-Applications 1.0.8, it gives ImportError: No module named 'keras.applications.resnext'. I try to change it to `from keras_applications.resnext import preproces

Brew is listed as the recommended way of installing httpie on macOS. However, it doesn't work with auth plugins.

For example, if you pip3 install requests-hawk and then run http --help hawk will not show as an auth type. If you pip3 install httpie-oauth it will install httpie via pip as a dependency and overwrite the brew installed link in /usr/local/bin/http and now all the plugins wil

SUMMARY

- include_tasks: included.yml
  loop:
    - 1
    - 2

Expected output:

TASK [include_tasks] ******************************
included: …/included.yml for localhost => (item=1)
included: …/included.yml for localhost => (item=2)

Current output:

TASK [include_tasks] ******************************
included: …/included.yml for localhost
included: …/in

tldr; in multi-process environment (Celery) sessions might lead to request/responses being mixed up.

It is unsafe to use Session in a multi-process environment - if the fork happens after Session initialisation the underlying connection pool will be shared across both processes, leading to potentially dangerous and hard to debug issues.

I'm not sure what should happen - whather a code change

I'm clearly overlooking something because I'm very confused.
Right now the default for values_format is .2g with seems not ideal as it formats 110 as 1.1+e01:

"{:.2g}".format(110)

'1.1e+02'

Changing the precision doesn't really help.
There's a pretty easy way to fix this, but not with any standard python formatting from what I can see:
https://stackoverflow.com/qu

Versions Affected:

All versions after PR #2082 when flags were added to Request. (>1.4.0)

Response.follow() method not consistent with Request.init(). Missing flags.

No sure how this got missed since PR #2082 was merged, but looking at the parameters for Request.__init__ and Response.follow it appears flags wasn't added to Response.follow to keep it in line with ho

Current implementation does sequential sigmoid_out and mul_. We can get better performance by fusing this operations together.

你的朋友正在使用键盘输入他的名字 name。偶尔，在键入字符 c 时，按键可能会被长按，而字符可能被输入 1 次或多次。

你将会检查键盘输入的字符 typed。如果它对应的可能是你的朋友的名字（其中一些字符可能被长按），那么就返回 True。

示例 1：

输入：name = "alex", typed = "aaleex"
输出：true
解释：'alex' 中的 'a' 和 'e' 被长按。
示例 2：

输入：name = "saeed", typed = "ssaaedd"
输出：false
解释：'e' 一定需要被键入两次，但在 typed 的输出中不是这样。
示例 3：

输入：name = "leelee", typed = "lleeelee"
输出：true
示例 4：

输入：name = "laiden", typ

When testing our Apache plugin on CentOS 6, I was surprised to find a "B" score from https://www.ssllabs.com/. The reason this happened is that SSL Labs tries to connect to your site without SNI and includes your server's behavior without SNI in your score.

Our Apache plugin has always expected connecting clients to support SNI and I do not think we need to change this behavior. Because of it a

Code Sample, a copy-pastable example if possible

# Your code here
import pandas as pd
import io

content = io.StringIO('''
time,val
212.23, 32
''')

date_cols = ['time']

df = pd.read_csv(
    content,
    sep=',',
    usecols=['val'],
    dtype= { 'val': int },
    parse_dates=date_cols,
)

Problem description

triggers

Traceback (most recen