class_docstr: A traffic forecasting dataset based on Los Angeles
  Metropolitan traffic conditions. The dataset contains traffic
  readings collected from 207 loop detectors on highways in Los Angeles
  County in aggregated 5 minute intervals for 4 months between March 2012
  to June 2012.
  
  For further details on the version of the sensor network and
  discretization 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/metr_la.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 METRLADatasetLoader obj's underlying member method code with example usage: obj._download_url(url, save_path) without return types