<jupyter_start><jupyter_text>Fixes Implementations  Virtual Notebook ImplementationUse an intersection observer, placeholder for non rendered cells, and loading indicator if notebook not fully rendered, see [2.2.x Virtual notebook](https://github.com/jupyterlab/jupyterlab/pull/8972).  Content Visibility ImplementationFor better performance while opening and switching a Notebook, we can use the new content-visibility supported by Chrome 85+ (https://web.dev/content-visibility), see PR [2.2.x Add content-visibility on Cell CSS class](https://github.com/jupyterlab/jupyterlab/pull/8970).![](images/content-visibility/content-visiblity-no-shadow-dom.png "")  Block Div ImplementationFor better performance when opening notebook, an option is to use block display instead of flex display, see PR [2.2.x Use block display in Notebook panel](https://github.com/jupyterlab/jupyterlab/pull/8968).![](images/div-block/div-block.png "")  Server Parsing ImplementationAdd the possibility of using fastjsonschema if installed and other libraries if installed or defined by a NBFORMAT_VALIDATOR environment variable, see PR [NbFormat: Use fastjsonschema](https://github.com/jupyter/nbformat/pull/191).  Shadow DOM ImplementationShadow DOM has been implemented in master branch but removed afterwards as conflicting with other fixes as Content Visibility, read more on [Revert "Move CodeMirror HTML tree and related CSS to shadow DOM"](https://github.com/jupyterlab/jupyterlab/pull/9015).<jupyter_code>import pandas as pd
from vega import VegaLite
diff = pd.read_csv('./datasets/diff.csv')
VegaLite({
  "data": {
    "url": "./datasets/diff.csv",
    "formdfat": {
      "type": "csv",
      "parse": {
        "confidenceInterval": "number",
        "mean": "number",
        "n": "number"
      }
    }
  },
  "facet": {
    "row": { "field": "mode", "type": "nominal" },
    "column": { "field": "type" }
  },
  "spec": {
    "encoding": {
      "color": {
        "field": "browser",
        "type": "nominal"
      },
      "x": {
        "field": "n",
        "type": "quantitative",
        "scale": {}
      }
    },
    "layer": [
      {
        "mark": {
          "type": "errorband",
          "clip": True
        },
        "encoding": {
          "y": {
            "field": "mean",
            "type": "quantitative",
            "scale": { "domain": [0, 2] },
            "title": "Ratio of Time (95% CI)"
          },
          "yError": { "field": "confidenceInterval" }
        }
      },
      {
        "mark": {
          "type": "line",
          "clip": True,
          "tooltip": True,
          "size": 1
        },
        "encoding": {
          "y": {
            "field": "mean",
            "type": "quantitative"
          }
        }
      }
    ]
  }
}
, diff)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1419950301739439, 'fraction_numerical': 0.011359602413915513, 'mean_word_length': 2.5943877551020407, 'pattern_counts': {'":': 45, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 6, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '142582', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 873, 'n_tokens_neox': 840, 'n_words': 232, 'path': 'docs/source/fixes/implementations.ipynb', 'repo_name': 'williamstein/benchmarks', 'size': '2817'}