link which I want to edit: https://www.tensorflow.org/community/contribute/docs
The description of Digit classifier is incorrect

The following do not yet have any pytests:

• boolean_algebra
• digital_image_processing
• divide_and_conquer
• hashes
• linear_algebra #1124
• networking_flow #1126
• traversals
• file_transfer_protocol # This might be difficult
• machine_learning
• neural_network

For ease of review, please only modify one directory per pull request.

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

There's a small mistake in the description of the embedding layer. It says

'Turns positive integers (indexes) into dense vectors of fixed size.'

but it should read

'Turns non-negative integers (indexes) into dense vectors of fixed size.'

as it expects indexes ranging from 0 to input_dim - 1.

See https://keras.io/layers/embeddings/

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

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

SUMMARY

(more a wishlist, not a bug actually)

Being https://bugs.python.org/issue2466 is fixed , someone w

https://github.com/scikit-learn/scikit-learn/blob/58289bc306f5547790d3bbc2190bdbbb5c582321/sklearn/impute/_iterative.py#L420

ImputerTriplets in fitted imputation_sequence lists are appended according to imputation_order, but order is inverted

There are several places in the documentation and comments where we use the words ‘constructor’ or ‘initializer’ to refer to the __init__ method of a class. We should change the wording to explicitly say ‘__init__ method’ instead.

Related to #2354.

Context

We would like to add torch::nn::functional::normalize to the C++ API, so that C++ users can easily find the equivalent of Python API torch.nn.functional.normalize.

Steps

• Add torch::nn::NormalizeOptions to torch/csrc/api/include/torch/nn/options/normalization.h (add this file if it doesn’t exist), which should include the following parameters (based on https://pytorch.

I think we should remove the code we deprecated in certbot/certbot#7198.

ref #29165 the stat_ops benchmarks add a significant amount of time to the ASV runs. The "kendall" correlation method in particular takes over 10 seconds per run in some cases, and there are at least 4 occurrences of this. So for that method alone 40s across 4 runs per benchmark suite would be close to 3 minutes just for those benchmarks

There are a few others mixed in there as well that can b

给定一个原区间[x, y] (y>=x) 和 N个无序的目标区间[x1, y1],[x2, y2],[x3, y3] ... [xn, yn] 判断原区间是否在目标区间内（即\bigcup_{i=1}^{n}[x_i, y_i]是否成立）

例如给定原区间[1,6]和无序的目标区间[2, 3] , [1, 2], [3, 9]. 即可认为区间[1, 6]在[2, 3] , [1, 2], [3, 9]内， 如图所示：