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 get_sensor_distances(self):
      coords_1010 = pd.read_csv("standard_1010.tsv.txt", sep="\t")
      num_edges = self.edge_index.shape[1]
      distances = []
      for edge_idx in range(num_edges):
          sensor1_idx = self.edge_index[0, edge_idx]
          sensor2_idx = self.edge_index[1, edge_idx]
          dist = self.get_geodesic_distance(
              sensor1_idx, sensor2_idx, coords_1010
          )
          distances.append(dist)
      assert len(distances) == num_edges
      return distances
  
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 2 for calling the EEGGraphDataset obj's underlying member method code with example usage: obj.get_sensor_distances() and returns: distances