code: def __call__(self, data):
      data = copy.copy(data)
      device = data.edge_index.device
  
      row = torch.arange(
          data.num_nodes, dtype=torch.long, device=device
      )
      col = torch.arange(
          data.num_nodes, dtype=torch.long, device=device
      )
  
      row = row.view(-1, 1).repeat(1, data.num_nodes).view(-1)
      col = col.repeat(data.num_nodes)
      edge_index = torch.stack([row, col], dim=0)
  
      edge_attr = None
      if data.edge_attr is not None:
          idx = data.edge_index[0] * data.num_nodes + data.edge_index[1]
          size = list(data.edge_attr.size())
          size[0] = data.num_nodes * data.num_nodes
          edge_attr = data.edge_attr.new_zeros(size)
          edge_attr[idx] = data.edge_attr
  
      edge_index, edge_attr = remove_self_loops(edge_index, edge_attr)
      data.edge_attr = edge_attr
      data.edge_index = edge_index
  
      return data
  
file_path: pytorch_geometric/examples/qm9_nn_conv.py
imports: import copy
  import os.path
  import torch
  import torch.nn.functional
  from torch.nn import GRU, Linear, ReLU, Sequential
  import torch_geometric.transforms
  from torch_geometric.datasets import QM9
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import NNConv, Set2Set
  from torch_geometric.utils import remove_self_loops
label_desc: Use image node_id 1 for calling the Complete obj's underlying member method code with example usage: obj.__call__(data) and returns: data