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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docstr: Returning the PedalMe London demand data iterator.
  
  Args types:
      * **lags** *(int)* - The number of time lags.
  Return types:
      * **dataset** *(StaticGraphTemporalSignal)* - The PedalMe dataset.
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code: def get_dataset(self, lags: int = 4) -> StaticGraphTemporalSignal:
      """Returning the PedalMe London demand data iterator.
  
      Args types:
          * **lags** *(int)* - The number of time lags.
      Return types:
          * **dataset** *(StaticGraphTemporalSignal)* - The PedalMe dataset.
      """
      self.lags = lags
      self._get_edges()
      self._get_edge_weights()
      self._get_targets_and_features()
      dataset = StaticGraphTemporalSignal(
          self._edges, self._edge_weights, self.features, self.targets
      )
      return dataset
  
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 6 for calling the PedalMeDatasetLoader obj's underlying member method code with example usage: obj.get_dataset(lags) and returns: dataset