code: def create_link_label(pos_edge_index, neg_edge_index):
      """
      Create labels for link prediction, based on positive and negative edges
  
      Args:
          pos_edge_index (torch.tensor): Positive edge index [2, num_edges]
          neg_edge_index (torch.tensor): Negative edge index [2, num_edges]
  
      Returns: Link label tensor, [num_positive_edges + num_negative_edges]
  
      """
      num_links = pos_edge_index.size(1) + neg_edge_index.size(1)
      link_labels = torch.zeros(
          num_links, dtype=torch.float, device=pos_edge_index.device
      )
      link_labels[: pos_edge_index.size(1)] = 1.0
      return link_labels
  
file_path: GraphGym/graphgym/models/transform.py
imports: import networkx
  import torch
  from torch_geometric.utils import negative_sampling
label_desc: Use image node_id 5 for calling a global function with example usage: create_link_label(pos_edge_index, neg_edge_index) and returns: link_labels