class_docstr: Ogbn Arxiv Dataset import and implementation.
  
  Attributes:
  
      graph (pgl.Graph): The :code:`Graph` data object.
  
      feature (numpy.ndarray): The feature of all nodes.
  
      y (numpy.ndarray): Labels for each nodes.
  
      num_classes (int): Number of classes.
  
      train_index (numpy.ndarray): The index for nodes in training set.
  
      val_index (numpy.ndarray): The index for nodes in validation set.
  
      test_index (numpy.ndarray): The index for nodes in test set.
class_docstr_tok: ["Ogbn","Arxiv","Dataset","import","and","implementation",".","Attributes",":","graph","(","pgl.Graph",")",":","The",":","code",":","`","Graph","`","data","object",".","feature","(","numpy.ndarray",")",":","The","feature","of","all","nodes",".","y","(","numpy.ndarray",")",":","Labels","for","each","nodes",".","num_classes","(","int",")",":","Number","of","classes",".","train_index","(","numpy.ndarray",")",":","The","index","for","nodes","in","training","set",".","val_index","(","numpy.ndarray",")",":","The","index","for","nodes","in","validation","set",".","test_index","(","numpy.ndarray",")",":","The","index","for","nodes","in","test","set","."]
code: def __init__(self):
      try:
          from ogb.nodeproppred import NodePropPredDataset
      except:
          raise ImportError(
              "Please run `pip install ogb` to install ogb library."
          )
  
      self.dataset = NodePropPredDataset(name="ogbn-arxiv")
      self._load_data()
  
imports: import os
  import io
  import sys
  import numpy
  import pickle
  from pgl.graph import Graph
label_desc: Use image node_id 1 to create a new OgbnArxivDataset object from inherited base classes: object with example: obj = OgbnArxivDataset()