code: def generate_signal(
      snapshot_count,
      n_count,
      feature_count,
      additional_features_keys=[],
  ):
      edge_indices = [
          get_edge_array(n_count) for _ in range(snapshot_count)
      ]
      edge_weights = [
          np.ones(edge_indices[t].shape[1])
          for t in range(snapshot_count)
      ]
      features = [
          np.random.uniform(0, 1, (n_count, feature_count))
          for _ in range(snapshot_count)
      ]
  
      if additional_features_keys:
          additional_features = {
              key: [
                  np.random.uniform(0, 1, (n_count, feature_count))
                  for _ in range(snapshot_count)
              ]
              for key in additional_features_keys
          }
          return (
              edge_indices,
              edge_weights,
              features,
              additional_features,
          )
  
      return edge_indices, edge_weights, features
  
imports: import numpy
  import networkx
  from torch_geometric_temporal.signal import temporal_signal_split
  from torch_geometric_temporal.signal import StaticGraphTemporalSignal
  from torch_geometric_temporal.signal import DynamicGraphTemporalSignal
  from torch_geometric_temporal.signal import DynamicGraphStaticSignal
  from torch_geometric_temporal.signal import StaticHeteroGraphTemporalSignal
  from torch_geometric_temporal.signal import DynamicHeteroGraphTemporalSignal
  from torch_geometric_temporal.signal import DynamicHeteroGraphStaticSignal
  from torch_geometric_temporal.dataset import METRLADatasetLoader, PemsBayDatasetLoader
  from torch_geometric_temporal.dataset import ChickenpoxDatasetLoader, PedalMeDatasetLoader, WikiMathsDatasetLoader, EnglandCovidDatasetLoader
  from torch_geometric_temporal.dataset import TwitterTennisDatasetLoader, MontevideoBusDatasetLoader, MTMDatasetLoader
  from torch_geometric_temporal.dataset import WindmillOutputLargeDatasetLoader, WindmillOutputMediumDatasetLoader, WindmillOutputSmallDatasetLoader
label_desc: Use image node_id 2 for calling a global function with example usage: generate_signal(snapshot_count, n_count, feature_count, additional_features_keys) and returns: edge_indices,  edge_weights,  features, edge_indices,  edge_weights,  features,  additional_features