code: def get_edge_array(n_count):
      return np.array(
          [edge for edge in nx.gnp_random_graph(n_count, 0.1).edges()]
      ).T
  
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 1 for calling a global function with example usage: get_edge_array(n_count) and returns: np