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

A description is incomplete. It should mention:

These patterns are not competing, but complementing each other. To achieve availability, one needs both fail-over and replication.

right after

"There are two main patterns to support high availability: fail-over and replication. "

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

Please implement the latest examples in the examples/ folder to the docs/mkdocs.yml to see theses examples on the homepage. Perhaps a subsectioning for that many examples is necessary. See Homepage

Thank you!

In = syntax,

• double quotes (")
• back slashes (\)
• non-ascii characters

are escaped in json.dumps:

$ http -v httpbin.org/post                                      \
    dquote='\"'                                                 \
    multi-line='line 1\nline 2'                               

We're trying to fix as many broken links as possible before modules move into collections. This is the batch of broken links on some Ansible modules.

NOTE: the link checker sometimes reports an error where a link actually works. Ignore those if you find them.

ISSUE TYPE

• Documentation Report

COMPONENT NAME

It could help potential new contributors if the "How to Contribute" section included the content or a link to the "Good Bug Reports" section of the contributing.md and a link to some simple example tests in step 4

scikit-learn/scikit-learn#16404 adds the Bunch object to the public API docs. However, the API docs do not reference Bunch where it's used.

It'd be nice to fix those and link to the now available Bunch page.

https://github.com/aymericdamien/TensorFlow-Examples/blob/9e1bb504f5d0f209d000997ce2ad95bb891798ab/examples/3_NeuralNetworks/bidirectional_rnn.py#L81

I think "outputs [-1]" and "outputs [0]" are equivalent (reversed) in this line of code, but the former (89%) works better than the latter (86%). Why?

🐛 Bug

To Reproduce

Run following from jupyter lab console

import torch
foo = torch.arange(5)
foo.as_strided((5,), (-1,), storage_offset=4)

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/daniil/.local/lib/python3.6/site-packages/torch/tensor.py", line 159, i

We have a few references in the documentation where we use the old name of that OS. We should update them.

• face_recognition version: latest docs
• Python version: N/A
• Operating System: N/A

Description

the docs say that default value for 'model' is "large". in fact, it is "small", which makes a different. please correct the docs

The problem

The google_travel_time documentation states the following:

By default, the sensor will update the travel time every 5 minutes

and:

For example, if you want to update sensor.morning_commute every 2 minutes on weekday mornings, you can use the following automation...

[but the code](https://github.com

Description

The instructor in the above mentioned video has created a new version of the same tutorial, which can be found here

Why

It is always good to keep resources and tutorials up-to-date. The new video talks about namespaces, chroot and cgroups, and speaks about containers at a greater depth.

Is this something you're interest

Describe the bug
can't find "from sklearn.cross_validation import train_test_split" in Latest version scikit-learn
To Reproduce
Steps to reproduce the behavior:

• Day1
• Step 5: Splitting the datasets into training sets and Test sets
• Can't find "from sklearn.cross_validation import train_test_split" in Latest version scikit-learn**

**Desktop (please complete the following infor

假如有一排房子，共 n 个，每个房子可以被粉刷成红色、蓝色或者绿色这三种颜色中的一种，你需要粉刷所有的房子并且使其相邻的两个房子颜色不能相同。

当然，因为市场上不同颜色油漆的价格不同，所以房子粉刷成不同颜色的花费成本也是不同的。每个房子粉刷成不同颜色的花费是以一个 n x 3 的矩阵来表示的。

例如，costs[0][0] 表示第 0 号房子粉刷成红色的成本花费；costs[1][2] 表示第 1 号房子粉刷成绿色的花费，以此类推。请你计算出粉刷完所有房子最少的花费成本。

注意：

所有花费均为正整数。

示例：

输入: [[17,2,17],[16,16,5],[14,3,19]]
输出: 10
解释: 将 0 号房子粉刷成蓝色，1 号房子粉刷成绿色，2 号房子粉刷成蓝色。
  最少花费: 2 + 5 + 3 = 10。

题目地址： ht

As seen with the test modifications in certbot/certbot#7771, there are lot of individual tests duplicating the same changes to the available modules such adding mod_ssl before testing a lot of installer functionality or the rewrite_module before testing redirects.

This code being duplicated makes it harder t

I think listing anti-patterns with some basic reasoning about "why not" is a good idea.

Example - singleton. Although #256 has "won't fix" label

• it is in PRs section, and people (if searching history at all) are searching issues first.
• it was misspelled, Singelton instead of Singleton, therefore impossible to find

Listing most popular anti-patterns (without actual implementation) shou

Code Sample, a copy-pastable example if possible

string = "HolyMoly'"
store.select('df','index == string')

#### Problem description
string = "HolyMoly'" do not work 

pandas 1.0.1

In the PCA section there is the following quote:

We see that these 150 components account for just over 90% of the variance.

While not inaccurate (150 componen