class_docstr: The `"Long Range Graph Benchmark (LRGB)"
  <https://arxiv.org/abs/2206.08164>`_
  datasets which is a collection of 5 graph learning datasets with tasks
  that are based on long-range dependencies in graphs. See the original
  `source code <https://github.com/vijaydwivedi75/lrgb>`_ for more details
  on the individual datasets.
  
  +------------------------+-------------------+----------------------+
  | Dataset                | Domain            | Task                 |
  +========================+===================+======================+
  | :obj:`PascalVOC-SP`    | Computer Vision   | Node Classification  |
  +------------------------+-------------------+----------------------+
  | :obj:`COCO-SP`         | Computer Vision   | Node Classification  |
  +------------------------+-------------------+----------------------+
  | :obj:`PCQM-Contact`    | Quantum Chemistry | Link Prediction      |
  +------------------------+-------------------+----------------------+
  | :obj:`Peptides-func`   | Chemistry         | Graph Classification |
  +------------------------+-------------------+----------------------+
  | :obj:`Peptides-struct` | Chemistry         | Graph Regression     |
  +------------------------+-------------------+----------------------+
  
  Args:
      root (str): Root directory where the dataset should be saved.
      name (str): The name of the dataset (one of :obj:`"PascalVOC-SP"`,
          :obj:`"COCO-SP"`, :obj:`"PCQM-Contact"`, :obj:`"Peptides-func"`,
          :obj:`"Peptides-struct"`)
      split (str, optional): If :obj:`"train"`, loads the training dataset.
          If :obj:`"val"`, loads the validation dataset.
          If :obj:`"test"`, loads the test dataset.
          (default: :obj:`"train"`)
      transform (callable, optional): A function/transform that takes in an
          :obj:`torch_geometric.data.Data` object and returns a transformed
          version. The data object will be transformed before every access.
          (default: :obj:`None`)
      pre_transform (callable, optional): A function/transform that takes in
          an :obj:`torch_geometric.data.Data` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
      pre_filter (callable, optional): A function that takes in an
          :obj:`torch_geometric.data.Data` object and returns a boolean
          value, indicating whether the data object should be included in the
          final dataset. (default: :obj:`None`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
  
  **STATS:**
  
  .. list-table::
      :widths: 15 10 10 10 10
      :header-rows: 1
  
      * - Name
        - #graphs
        - #nodes
        - #edges
        - #classes
      * - PascalVOC-SP
        - 11,355
        - ~479.40
        - ~2,710.48
        - 21
      * - COCO-SP
        - 123,286
        - ~476.88
        - ~2,693.67
        - 81
      * - PCQM-Contact
        - 529,434
        - ~30.14
        - ~61.09
        - 1
      * - Peptides-func
        - 15,535
        - ~150.94
        - ~307.30
        - 10
      * - Peptides-struct
        - 15,535
        - ~150.94
        - ~307.30
        - 11
class_docstr_tok: ["The","`","``","Long","Range","Graph","Benchmark","(","LRGB",")","''","<","https",":","\/\/arxiv.org\/abs\/2206.08164",">","`","_","datasets","which","is","a","collection","of","5","graph","learning","datasets","with","tasks","that","are","based","on","long-range","dependencies","in","graphs",".","See","the","original","`","source","code","<","https",":","\/\/github.com\/vijaydwivedi75\/lrgb",">","`","_","for","more","details","on","the","individual","datasets",".","+","--","--","--","--","--","--","--","--","--","--","--","--","+","--","--","--","--","--","--","--","--","--","-+","--","--","--","--","--","--","--","--","--","--","--","+","|","Dataset","|","Domain","|","Task","|","+========================+===================+======================+","|",":","obj",":","`","PascalVOC-SP","`","|","Computer","Vision","|","Node","Classification","|","+","--","--","--","--","--","--","--","--","--","--","--","--","+","--","--","--","--","--","--","--","--","--","-+","--","--","--","--","--","--","--","--","--","--","--","+","|",":","obj",":","`","COCO-SP","`","|","Computer","Vision","|","Node","Classification","|","+","--","--","--","--","--","--","--","--","--","--","--","--","+","--","--","--","--","--","--","--","--","--","-+","--","--","--","--","--","--","--","--","--","--","--","+","|",":","obj",":","`","PCQM-Contact","`","|","Quantum","Chemistry","|","Link","Prediction","|","+","--","--","--","--","--","--","--","--","--","--","--","--","+","--","--","--","--","--","--","--","--","--","-+","--","--","--","--","--","--","--","--","--","--","--","+","|",":","obj",":","`","Peptides-func","`","|","Chemistry","|","Graph","Classification","|","+","--","--","--","--","--","--","--","--","--","--","--","--","+","--","--","--","--","--","--","--","--","--","-+","--","--","--","--","--","--","--","--","--","--","--","+","|",":","obj",":","`","Peptides-struct","`","|","Chemistry","|","Graph","Regression","|","+","--","--","--","--","--","--","--","--","--","--","--","--","+","--","--","--","--","--","--","--","--","--","-+","--","--","--","--","--","--","--","--","--","--","--","+","Args",":","root","(","str",")",":","Root","directory","where","the","dataset","should","be","saved",".","name","(","str",")",":","The","name","of","the","dataset","(","one","of",":","obj",":","`","``","PascalVOC-SP","''","`",",",":","obj",":","`","``","COCO-SP","''","`",",",":","obj",":","`","``","PCQM-Contact","''","`",",",":","obj",":","`","``","Peptides-func","''","`",",",":","obj",":","`","``","Peptides-struct","''","`",")","split","(","str",",","optional",")",":","If",":","obj",":","`","``","train","''","`",",","loads","the","training","dataset",".","If",":","obj",":","`","``","val","''","`",",","loads","the","validation","dataset",".","If",":","obj",":","`","``","test","''","`",",","loads","the","test","dataset",".","(","default",":",":","obj",":","`","``","train","''","`",")","transform","(","callable",",","optional",")",":","A","function\/transform","that","takes","in","an",":","obj",":","`","torch_geometric.data.Data","`","object","and","returns","a","transformed","version",".","The","data","object","will","be","transformed","before","every","access",".","(","default",":",":","obj",":","`","None","`",")","pre_transform","(","callable",",","optional",")",":","A","function\/transform","that","takes","in","an",":","obj",":","`","torch_geometric.data.Data","`","object","and","returns","a","transformed","version",".","The","data","object","will","be","transformed","before","being","saved","to","disk",".","(","default",":",":","obj",":","`","None","`",")","pre_filter","(","callable",",","optional",")",":","A","function","that","takes","in","an",":","obj",":","`","torch_geometric.data.Data","`","object","and","returns","a","boolean","value",",","indicating","whether","the","data","object","should","be","included","in","the","final","dataset",".","(","default",":",":","obj",":","`","None","`",")","force_reload","(","bool",",","optional",")",":","Whether","to","re-process","the","dataset",".","(","default",":",":","obj",":","`","False","`",")","*","*","STATS",":","*","*","..","list-table",":",":",":","widths",":","15","10","10","10","10",":","header-rows",":","1","*","-","Name","-","#","graphs","-","#","nodes","-","#","edges","-","#","classes","*","-","PascalVOC-SP","-","11,355","-","~479.40","-","~2,710.48","-","21","*","-","COCO-SP","-","123,286","-","~476.88","-","~2,693.67","-","81","*","-","PCQM-Contact","-","529,434","-","~30.14","-","~61.09","-","1","*","-","Peptides-func","-","15,535","-","~150.94","-","~307.30","-","10","*","-","Peptides-struct","-","15,535","-","~150.94","-","~307.30","-","11"]
code: def label_remap_coco(self) -> Dict[int, int]:
      # Util function for name 'COCO-SP'
      # to remap the labels as the original label idxs are not contiguous
      original_label_idx = [
          0,
          1,
          2,
          3,
          4,
          5,
          6,
          7,
          8,
          9,
          10,
          11,
          13,
          14,
          15,
          16,
          17,
          18,
          19,
          20,
          21,
          22,
          23,
          24,
          25,
          27,
          28,
          31,
          32,
          33,
          34,
          35,
          36,
          37,
          38,
          39,
          40,
          41,
          42,
          43,
          44,
          46,
          47,
          48,
          49,
          50,
          51,
          52,
          53,
          54,
          55,
          56,
          57,
          58,
          59,
          60,
          61,
          62,
          63,
          64,
          65,
          67,
          70,
          72,
          73,
          74,
          75,
          76,
          77,
          78,
          79,
          80,
          81,
          82,
          84,
          85,
          86,
          87,
          88,
          89,
          90,
      ]
  
      label_map = {}
      for i, key in enumerate(original_label_idx):
          label_map[key] = i
  
      return label_map
  
file_path: pytorch_geometric/torch_geometric/datasets/lrgb.py
imports: import os
  import os.path
  import pickle
  from typing import Callable, Dict, List, Optional
  import torch
  from tqdm import tqdm
  from torch_geometric.data import Data, InMemoryDataset, download_url, extract_zip
  from torch_geometric.io import fs
label_desc: Use image node_id 8 for calling the LRGBDataset obj's underlying member method code with example usage: obj.label_remap_coco() and returns: label_map