class_docstr: Defining the Graph for the Two-Stream Adaptive Graph Convolutional Network.
  It's composed of the normalized inward-links, outward-links and
  self-links between the nodes as originally defined in the
  `authors repo  <https://github.com/lshiwjx/2s-AGCN/blob/master/graph/tools.py>`
  resulting in the shape of (3, num_nodes, num_nodes).
  Args:
      edge_index (Tensor array): Edge indices
      num_nodes (int): Number of nodes
  Return types:
          * **A** (PyTorch Float Tensor) - Three layer normalized adjacency matrix
class_docstr_tok: ["Defining","the","Graph","for","the","Two-Stream","Adaptive","Graph","Convolutional","Network",".","It","'s","composed","of","the","normalized","inward-links",",","outward-links","and","self-links","between","the","nodes","as","originally","defined","in","the","`","authors","repo","<","https",":","\/\/github.com\/lshiwjx\/2s-AGCN\/blob\/master\/graph\/tools.py",">","`","resulting","in","the","shape","of","(","3",",","num_nodes",",","num_nodes",")",".","Args",":","edge_index","(","Tensor","array",")",":","Edge","indices","num_nodes","(","int",")",":","Number","of","nodes","Return","types",":","*","*","*","A","*","*","(","PyTorch","Float","Tensor",")","-","Three","layer","normalized","adjacency","matrix"]
code: def get_spatial_graph(self, num_nodes):
      self_mat = torch.eye(num_nodes)
      inward_mat = torch.squeeze(to_dense_adj(self.edge_index))
      inward_mat_norm = F.normalize(inward_mat, dim=0, p=1)
      outward_mat = inward_mat.transpose(0, 1)
      outward_mat_norm = F.normalize(outward_mat, dim=0, p=1)
      adj_mat = torch.stack(
          (self_mat, inward_mat_norm, outward_mat_norm)
      )
      return adj_mat
  
imports: import math
  import torch
  import numpy
  import torch.nn
  from torch.autograd import Variable
  from torch_geometric.utils.to_dense_adj import to_dense_adj
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the GraphAAGCN obj's underlying member method code with example usage: obj.get_spatial_graph(num_nodes) and returns: adj_mat