code: def create_hetero_mock_data(n_count, feature_dict):
      _x_dict = {
          "author": torch.FloatTensor(
              np.random.uniform(0, 1, (n_count, feature_dict["author"]))
          ),
          "paper": torch.FloatTensor(
              np.random.uniform(0, 1, (n_count, feature_dict["paper"]))
          ),
      }
      _edge_index_dict = {
          ("author", "writes", "paper"): torch.LongTensor(
              get_edge_array(n_count)
          )
      }
  
      data = HeteroData()
      data["author"].x = _x_dict["author"]
      data["paper"].x = _x_dict["paper"]
      data[("author", "writes", "paper")].edge_index = _edge_index_dict[
          ("author", "writes", "paper")
      ]
      data = T.ToUndirected()(data)
  
      return data.x_dict, data.edge_index_dict, data.metadata()
  
file_path: pytorch_geometric_temporal/test/heterogeneous_test.py
imports: import torch
  import numpy
  import networkx
  import torch_geometric.transforms
  from torch_geometric.data import HeteroData
  from torch_geometric_temporal.nn.hetero import HeteroGCLSTM
label_desc: Use image node_id 2 for calling a global function with example usage: create_hetero_mock_data(n_count, feature_dict) and returns: data,  data,  data