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if (Translator.BetterBibTeX) {
    if (tex.has.doi) {
        tex.remove("doi");
    }
    if (tex.has.url) {
        tex.remove("url");
    }
    if (tex.has.issn) {
        tex.remove("issn");
    }

dschaehi

type: ActionsTagsBackup
author: jaeheelee
platformVersion: 7.0.0-beta.68+c31a40c74
pluginVersion: 1.0.0-beta.35
timestamp: '2024-03-26T09:55:19.478Z'
actions:
  default0:
    event: 1
    operation: 1
    data: /unread

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\NeedsTeXFormat{LaTeX2e}
\ProvidesPackage{commenting}[2022/10/29 by Jae Hee Lee (http://jaeheelee.de)]
\RequirePackage{hyperref}
\RequirePackage{ifdraft}
\RequirePackage{graphicx}
\RequirePackage{xcolor}
\RequirePackage{xspace}
\RequirePackage{environ}
\RequirePackage{soul}
\RequirePackage{twemojis}

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model.zero_grad()                                   # Reset gradients tensors
for i, (inputs, labels) in enumerate(training_set):
    predictions = model(inputs)                     # Forward pass
    loss = loss_function(predictions, labels)       # Compute loss function
    loss = loss / accumulation_steps                # Normalize our loss (if averaged)
    loss.backward()                                 # Backward pass
    if (i+1) % accumulation_steps == 0:             # Wait for several backward steps
        optimizer.step()                            # Now we can do an optimizer step
        model.zero_grad()                           # Reset gradients tensors
        if (i+1) % evaluation_steps == 0:           # Evaluate the model when we...

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/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab

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{
    "shortcuts": [
        {
            "command": "notebook:toggle-all-cell-line-numbers",
            "keys": [
                "Alt L"
            ],
            "selector": ".jp-Notebook:focus"
        },        
        // Moving cells

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from collections import OrderedDict

from torchvision import models


def gen_feature_extractor(model, output_layer):
    layers = OrderedDict()
    for (k, v) in model._modules.items():
        layers[k] = v
        if k == output_layer:

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def flatten_json(json):
    if type(json) == dict:
        for k, v in list(json.items()):
            if type(v) == dict:
                flatten_json(v)
                json.pop(k)
                for k2, v2 in v.items():
                    json[k+"."+k2] = v2

                    

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import torch


def hamming_score(pred, answer):
    out = ((pred & answer).sum(dim=1) / (pred | answer).sum(dim=1)).mean()
    if out.isnan():
        out = torch.tensor(1.0)
    return out

answer = torch.tensor([[0, 1, 0], [0, 1, 1], [1, 0, 1], [0, 0, 1]])

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{
    "title": "Emacs keys",
    "rules": [
	{
            "description": "Change right_option + a to right_control + a",
	    "manipulators": [
		{
		    "type": "basic",
		    "from": {
			"key_code": "a",