code: def barabasi_albert_graph(
      num_nodes: int, num_edges: int
  ) -> torch.Tensor:
      r"""Returns the :obj:`edge_index` of a Barabasi-Albert preferential
      attachment model, where a graph of :obj:`num_nodes` nodes grows by
      attaching new nodes with :obj:`num_edges` edges that are preferentially
      attached to existing nodes with high degree.
  
      Args:
          num_nodes (int): The number of nodes.
          num_edges (int): The number of edges from a new node to existing nodes.
  
      Example:
          >>> barabasi_albert_graph(num_nodes=4, num_edges=3)
          tensor([[0, 0, 0, 1, 1, 2, 2, 3],
                  [1, 2, 3, 0, 2, 0, 1, 0]])
      """
      assert num_edges > 0 and num_edges < num_nodes
  
      row, col = torch.arange(num_edges), torch.randperm(num_edges)
  
      for i in range(num_edges, num_nodes):
          row = torch.cat(
              [row, torch.full((num_edges,), i, dtype=torch.long)]
          )
          choice = np.random.choice(
              torch.cat([row, col]).numpy(), num_edges
          )
          col = torch.cat([col, torch.from_numpy(choice)])
  
      edge_index = torch.stack([row, col], dim=0)
      edge_index, _ = remove_self_loops(edge_index)
      edge_index = to_undirected(edge_index, num_nodes=num_nodes)
  
      return edge_index
  
imports: import warnings
  from typing import List, Union
  import numpy
  import torch
  from torch_geometric.utils import remove_self_loops, to_undirected
label_desc: Use image node_id 3 for calling a global function with example usage: barabasi_albert_graph(num_nodes, num_edges) and returns: edge_index