class_docstr: A dataset of PedalMe Bicycle deliver orders in London between 2020
  and 2021. We made it public during the development of PyTorch Geometric
  Temporal. The underlying graph is static - vertices are localities and
  edges are spatial_connections. Vertex features are lagged weekly counts of the
  delivery demands (we included 4 lags). The target is the weekly number of
  deliveries the upcoming week. Our dataset consist of more than 30 snapshots (weeks).
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code: def _get_edges(self):
      self._edges = np.array(self._dataset["edges"]).T
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/dataset/pedalme.py
imports: import json
  import urllib
  import numpy
  from ..signal import StaticGraphTemporalSignal
label_desc: Use image node_id 3 for calling the PedalMeDatasetLoader obj's underlying member method code with example usage: obj._get_edges() without return types