piscisaureus

Locate the section for your github remote in the .git/config file. It looks like this:

[remote "origin"]
	fetch = +refs/heads/*:refs/remotes/origin/*
	url = [email protected]:joyent/node.git

Now add the line fetch = +refs/pull/*/head:refs/remotes/origin/pr/* to this section. Obviously, change the github url to match your project's URL. It ends up looking like this:

stuart11n

git branch -m old_branch new_branch         # Rename branch locally    
git push origin :old_branch                 # Delete the old branch    
git push --set-upstream origin new_branch   # Push the new branch, set local branch to track the new remote

masih

#!/bin/bash
 
# Script for installing Fish Shell on systems without root access.
# Fish Shell will be installed in $HOME/local/bin.
# It's assumed that wget and a C/C++ compiler are installed.
 
# exit on error
set -e
 
FISH_SHELL_VERSION=2.1.1

Zearin

NOTE: This is a question I found on StackOverflow which I’ve archived here, because the answer is so effing phenomenal.

---

Q: How can I make a chain of function decorators in Python?

---

If you are not into long explanations, see [Paolo Bergantino’s answer][2].

ericdouglas

// 4 spaces to 2 spaces
%s;^\(\s\+\);\=repeat(' ', len(submatch(0))/2);g

// Tab to 2 spaces
:%s/\t/  /g

t-vi

import torch.utils.data
from torchvision import datasets, transforms

class PartialDataset(torch.utils.data.Dataset):
    def __init__(self, parent_ds, offset, length):
        self.parent_ds = parent_ds
        self.offset = offset
        self.length = length
        assert len(parent_ds)>=offset+length, Exception("Parent Dataset not long enough")
        super(PartialDataset, self).__init__()

Kshitij09

import torch
from pytorch_lightning import Callback
from IPython.display import display, clear_output
import copy
import pandas as pd

def unwrap(x):
  if isinstance(x,torch.Tensor):
    return x.item()
  return x

jph00

All of the python 3.9 standard library

For a version without the collapsible details sections (so you can search the whole thing in your browser), click here.

abc

• abc.ABC
• abc.ABCMeta

mmahbub

df_test = pd.DataFrame(test_ds, columns=['text', 'author'])
# make a unique list of authors
auth = sorted(set(df['author']))
# make a dict of possible signatures where key is the labeled entity and value is a list of possible signatures
auth_dict = {}
auth_dict[auth[0]] = ['ben', 'benjamin', 'rogers', 'benjamin rogers','ben rogers','br']
auth_dict[auth[1]] = ['chris', 'dorland','chris dorland','cd']
auth_dict[auth[2]] = ['drew','fossum','drew fossum','df']
auth_dict[auth[3]] = ['jeffrey','shankman','jeffrey shankman','js']
auth_dict[auth[4]] = ['kevin','presto','kevin presto','kp']

Maharshi-Pandya

You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

## Core Principles

1. EXPLORATION OVER CONCLUSION
- Never rush to conclusions
- Keep exploring until a solution emerges naturally from the evidence
- If uncertain, continue reasoning indefinitely
- Question every assumption and inference