Current implementation of Go binding can not specify options.

GPUOptions struct is in internal package. And go generate doesn't work for protobuf directory. So we can't specify GPUOptions for NewSession.

We have a lot of is_prime (or similar) functions: https://github.com/TheAlgorithms/Python/search?p=4&q=is_prime, https://github.com/TheAlgorithms/Python/search?q=isPrime, data_structures/hashing/number_theory/prime_numbers, etc. Shall we use one common function for that exactly identical

Description

Since we have Kotlin tutorials for available, why not include Flutter?

Why

We should allow this option when considering mobile development.

Possible Implementation & Open Questions

Similar to other sections formats, add sources and links for people to study.

Is this something you're interested in working on?

• YES
• NO

https://github.com/pallets/flask/blob/9039534eee6a87da98a1dee9e4338d1b73e861f8/src/flask/ctx.py#L133

While it makes no sense to use it outside a context, the error message could be nicer when you do so anyway (e.g. because you accidentally call a function using it from a celery task or some other place without a request context), like e.g. in copy_current_request_context [in the same file](ht

🚀 Feature request

Currently, the EncoderDecoderModel class in PyTorch automatically creates the decoder_input_ids based on the labels provided by the user (similar to how this is done for T5/BART). This should also be implemented for TFEncoderDecoderModel, because currently users should manually provide decoder_input_ids to the model.

One can take a look at the TF implementation

🚀 Feature

alias to generate tensor with random uniform distribution.

Motivation

uniform distribution is one of the most commonly used distributions by users. currently generating tensor distributed uniformly can be done using tensor initializer (torch.FloatTensor(*size).uniform_(low, high)), or by definition:
(high - low) * torch.rand(*size) + low.

Pitch

Adding the alias

These examples take quite a long time to run, and they make our documentation CI fail quite frequently due to timeout. It'd be nice to speed the up a little bit.

To contributors: if you want to work on an example, first have a look at the example, and if you think you're comfortable working on it and have found a potential way to speed-up execution time while preserving the educational message

Screenshot

I've added a red vertical ruler so that you see the issue

Description

As already explained in numerous issues, the use of 'Inter' font is problematic, it does not allow to align dates for instance,
and does not play nice with numbers either.

In my supe

Description

Usage of scrapy.utils.response.response_httprepr inside DownloaderStats middleware causing application to make unnecessary memory allocation.
response_httprepr used only one time - to calculate response sizes fo

Hello everyone, I will explain the current status of manim in this issue.

Now there are three main manim versions, and their differences:

1. The master branch of 3b1b/manim: Rendering on GPU using OpenGL and moderngl. Support interaction and have higher efficiency.
2. ManimCommunity/manim: (@ManimCommunity

First check

• I added a very descriptive title to this issue.
• I used the GitHub search to find a similar issue and didn't find it.
• I searched the FastAPI documentation, with the integrated search.
• I already searched in Google "How to X in FastAPI" and didn't find any information.
• I already read and followed all the tutorial in the docs and didn't find an answ

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the master branch of pandas.

Reproducible Example

Running ./ci/code_checks.sh docstrings results in many warnings:

$ ./ci/