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>`_
docstr: Returns data iterator for PEMS-BAY dataset as an instance of the
  static graph temporal signal class.
  
  Return types:
      * **dataset** *(StaticGraphTemporalSignal)* - The PEMS-BAY traffic
          forecasting dataset.
docstr_tok: ["Returns","data","iterator","for","PEMS-BAY","dataset","as","an","instance","of","the","static","graph","temporal","signal","class",".","Return","types",":","*","*","*","dataset","*","*","*","(","StaticGraphTemporalSignal",")","*","-","The","PEMS-BAY","traffic","forecasting","dataset","."]
code: def get_dataset(
      self, num_timesteps_in: int = 12, num_timesteps_out: int = 12
  ) -> StaticGraphTemporalSignal:
      """Returns data iterator for PEMS-BAY dataset as an instance of the
      static graph temporal signal class.
  
      Return types:
          * **dataset** *(StaticGraphTemporalSignal)* - The PEMS-BAY traffic
              forecasting dataset.
      """
      self._get_edges_and_weights()
      self._generate_task(num_timesteps_in, num_timesteps_out)
      dataset = StaticGraphTemporalSignal(
          self.edges, self.edge_weights, self.features, self.targets
      )
      return dataset
  
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 6 for calling the PemsBayDatasetLoader obj's underlying member method code with example usage: obj.get_dataset(num_timesteps_in, num_timesteps_out) and returns: dataset