class_docstr: `NBA <https://github.com/EnyanDai/FairGNN/tree/main/dataset/NBA>`_ is an NBA on court performance dataset along salary, social engagement etc.
  
  :param data_path: The url where the dataset is found, defaults to :obj:`https://github.com/divelab/DIG_storage/raw/main/fairgraph/datasets/nba/`
  :type data_path: str, optional
  
  :param root: The path to root directory where the dataset is saved, defaults to :obj:`./dataset/nba`
  :type root: str, optional
class_docstr_tok: ["`","NBA","<","https",":","\/\/github.com\/EnyanDai\/FairGNN\/tree\/main\/dataset\/NBA",">","`","_","is","an","NBA","on","court","performance","dataset","along","salary",",","social","engagement","etc",".",":","param","data_path",":","The","url","where","the","dataset","is","found",",","defaults","to",":","obj",":","`","https",":","\/\/github.com\/divelab\/DIG_storage\/raw\/main\/fairgraph\/datasets\/nba\/","`",":","type","data_path",":","str",",","optional",":","param","root",":","The","path","to","root","directory","where","the","dataset","is","saved",",","defaults","to",":","obj",":","`",".\/dataset\/nba","`",":","type","root",":","str",",","optional"]
code: def process(self):
      (
          adj,
          features,
          labels,
          idx_train,
          idx_val,
          idx_test,
          sens,
          idx_sens_train,
      ) = self.read_graph()
      features = self.feature_norm(features)
  
      labels[labels > 1] = 1
      sens[sens > 0] = 1
  
      self.features = features.cuda()
      self.labels = labels.cuda()
      self.idx_train = idx_train.cuda()
      self.idx_val = idx_val.cuda()
      self.idx_test = idx_test.cuda()
      self.sens = sens.cuda()
      self.idx_sens_train = idx_sens_train.long().cuda()
  
      self.adj = adj
  
imports: import torch
  import numpy
  import os
  import pandas
  import scipy.sparse
  import random
  from torch_geometric.data import download_url
label_desc: Use image node_id 6 for calling the NBA obj's underlying member method code with example usage: obj.process() without return types