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 __init__(self, raw_data_dir=os.path.join(os.getcwd(), "data")):
      super(PemsBayDatasetLoader, self).__init__()
      self.raw_data_dir = raw_data_dir
      self._read_web_data()
  
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 1 to create a new PemsBayDatasetLoader object from inherited base classes: object with example: obj = PemsBayDatasetLoader(raw_data_dir)