code: def generate_heterogeneous_signal(
      snapshot_count, node_count, feature_count, graph_count
  ):
      edge_index_dicts = []
      edge_weight_dicts = []
      feature_dicts = []
      target_dicts = []
      batch_dicts = []
      for _ in range(snapshot_count):
          node_start = 0
          edge_index_dict_s = {("author", "writes", "paper"): []}
          edge_weight_dict_s = {("author", "writes", "paper"): []}
          feature_dict_s = {"author": [], "paper": []}
          target_dict_s = {"author": [], "paper": []}
          batch_dict_s = {"author": [], "paper": []}
          for i in range(graph_count):
              edge_index_dict_s[("author", "writes", "paper")].append(
                  get_edge_array(node_count, node_start)
              )
              edge_weight_dict_s[("author", "writes", "paper")].append(
                  (
                      np.ones(
                          edge_index_dict_s[
                              ("author", "writes", "paper")
                          ][-1].shape[0]
                      )
                  )
              )
              feature_dict_s["paper"].append(
                  np.random.uniform(0, 1, (node_count, feature_count))
              )
              feature_dict_s["author"].append(
                  np.random.uniform(0, 1, (node_count, feature_count))
              )
              target_dict_s["paper"].append(
                  np.array(
                      [
                          np.random.choice([0, 1])
                          for _ in range(node_count)
                      ]
                  )
              )
              target_dict_s["author"].append(
                  np.array(
                      [
                          np.random.choice([0, 1])
                          for _ in range(node_count)
                      ]
                  )
              )
              batch_dict_s["paper"].append(
                  np.array([i for _ in range(node_count)])
              )
              batch_dict_s["author"].append(
                  np.array([i for _ in range(node_count)])
              )
              node_start = node_start + node_count
          edge_index_dicts.append(
              {
                  node_type: np.concatenate(edge_indices_s).T
                  for node_type, edge_indices_s in edge_index_dict_s.items()
              }
          )
          edge_weight_dicts.append(
              {
                  node_type: np.concatenate(edge_weights_s)
                  for node_type, edge_weights_s in edge_weight_dict_s.items()
              }
          )
          feature_dicts.append(
              {
                  node_type: np.concatenate(features_s)
                  for node_type, features_s in feature_dict_s.items()
              }
          )
          target_dicts.append(
              {
                  node_type: np.concatenate(targets_s)
                  for node_type, targets_s in target_dict_s.items()
              }
          )
          batch_dicts.append(
              {
                  node_type: np.concatenate(batches_s)
                  for node_type, batches_s in batch_dict_s.items()
              }
          )
      return (
          edge_index_dicts,
          edge_weight_dicts,
          feature_dicts,
          target_dicts,
          batch_dicts,
      )
  
imports: import numpy
  import networkx
  from torch_geometric_temporal.signal import temporal_signal_split
  from torch_geometric_temporal.signal import StaticGraphTemporalSignalBatch
  from torch_geometric_temporal.signal import DynamicGraphTemporalSignalBatch
  from torch_geometric_temporal.signal import DynamicGraphStaticSignalBatch
  from torch_geometric_temporal.signal import StaticHeteroGraphTemporalSignalBatch
  from torch_geometric_temporal.signal import DynamicHeteroGraphTemporalSignalBatch
  from torch_geometric_temporal.signal import DynamicHeteroGraphStaticSignalBatch
label_desc: Use image node_id 3 for calling a global function with example usage: generate_heterogeneous_signal(snapshot_count, node_count, feature_count, graph_count) and returns: edge_index_dicts,  edge_weight_dicts,  feature_dicts,  target_dicts,  batch_dicts