class_docstr: Build graph, treat all nodes as the same type
  Parameters
  ----------
  x: edge weights of 8-node complete graph
      There are 1 x 64 edges
  y: labels (diseased/healthy)
  num_nodes: the number of nodes of the graph. In our case, it is 8.
  indices: Patient level indices. They are used to generate edge weights.
  
  Output
  ------
  a complete 8-node DGLGraph with node features and edge weights
code: def __len__(self):
      return len(self.indices)
  
file_path: dgl/examples/pytorch/eeg-gcnn/EEGGraphDataset.py
imports: import math
  from itertools import product
  import dgl
  import numpy
  import pandas
  import torch
  from dgl.data import DGLDataset
label_desc: Use image node_id 4 for calling the EEGGraphDataset obj's underlying member method code with example usage: obj.__len__() and returns: len