code: def tree(
      depth: int,
      branch: int = 2,
      undirected: bool = False,
      device: Optional[torch.device] = None,
  ) -> Tuple[Tensor, Tensor]:
      """Generates a tree graph with the given depth and branch size, along with
      node-level depth indicators.
  
      Args:
          depth (int): The depth of the tree.
          branch (int, optional): The branch size of the tree.
              (default: :obj:`2`)
          undirected (bool, optional): If set to :obj:`True`, the tree graph will
              be undirected. (default: :obj:`False`)
          device (torch.device, optional): The desired device of the returned
              tensors. (default: :obj:`None`)
      """
      edges: List[Tuple[int, int]] = []
      depths: List[int] = [0]
  
      def add_edges(node: int, current_depth: int) -> None:
          node_count = len(depths)
  
          if current_depth < depth:
              for i in range(branch):
                  edges.append((node, node_count + i))
                  depths.append(current_depth + 1)
  
              for i in range(branch):
                  add_edges(
                      node=node_count + i,
                      current_depth=current_depth + 1,
                  )
  
      add_edges(node=0, current_depth=0)
  
      edge_index = torch.tensor(edges, device=device).t().contiguous()
      if undirected:
          edge_index = to_undirected(edge_index, num_nodes=len(depths))
  
      return edge_index, torch.tensor(depths, device=device)
  
file_path: pytorch_geometric/torch_geometric/datasets/graph_generator/tree_graph.py
imports: from typing import List, Optional, Tuple
  import torch
  from torch import Tensor
  from torch_geometric.data import Data
  from torch_geometric.datasets.graph_generator import GraphGenerator
  from torch_geometric.utils import to_undirected
label_desc: Use image node_id 1 for calling a global function with example usage: tree(depth, branch, undirected, device) and returns: edge_index,  torch