class_docstr: A traffic forecasting dataset as described in Diffusion Convolution Layer Paper.
  
  This traffic dataset is collected by California Transportation Agencies (CalTrans)
  Performance Measurement System (PeMS). It is represented by a network of 325 traffic sensors
  in the Bay Area with 6 months of traffic readings ranging from Jan 1st 2017 to May 31th 2017
  in 5 minute intervals.
  
  For details see: `"Diffusion Convolutional Recurrent Neural Network:
  Data-Driven Traffic Forecasting" <https://arxiv.org/abs/1707.01926>`_
code: def _download_url(self, url, save_path):  # pragma: no cover
      with urllib.request.urlopen(url) as dl_file:
          with open(save_path, "wb") as out_file:
              out_file.write(dl_file.read())
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/dataset/pems_bay.py
imports: import os
  import urllib
  import zipfile
  import numpy
  import torch
  from torch_geometric.utils import dense_to_sparse
  from ..signal import StaticGraphTemporalSignal
label_desc: Use image node_id 2 for calling the PemsBayDatasetLoader obj's underlying member method code with example usage: obj._download_url(url, save_path) without return types