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System information

• TensorFlow version: 1.13.1
• Doc Link: https://www.tensorflow.org/api_docs/python/tf/cus

(e.g. for links and images), because some of these examples are now being rendered in the docs.

Added by @fchollet in requests for contributions.

There are some common misuse patterns in TorchScript that we should issue clear error messages for instead of generating generic error that doesn't capture root cause of error.

Here are a few examples:

• Attempting to construct a nn.Module inside TorchScript. This currently errors out because TorchScript would attempt to compile __init__() method of module, which usually contains a call

We should be using pkg_resources (or importlib.resources if our min Python version is 3.7) instead of uses of __file__.

$ get grep '__file__' sklearn/
sklearn/__check_build/__init__.py:    local_dir = os.path.split(__file__)[0]
sklearn/datasets/_base.py:    module_path = dirname(__file__)
sklearn/datasets/_base.py:    module_path = dirname(__file__)
sklearn/datasets/_base.py:    

nodejs/node@6820054 is how Node.js raises the file descriptor limit. Afterwards, soft limit == hard limit; there's no way for libuv retrieve the old soft limit again. Defaulting to 256 or 1024, depending on the platform, is the only way.

FWIW, Node.js has been doing that for 8 years and I don't recall a single bug report. I recently added similar logic to Deno (denoland/deno@d46b37f), again

Currently many more Python projects like dask and optuna are using Python type hints. With the Python package of xgboost gaining more and more features, we should also adopt mypy as a safe guard against some type errors and for better code documentation.