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: Uses the node features of the graph and generates a feature/target
  relationship of the shape
  (num_nodes, num_node_features, num_timesteps_in) -> (num_nodes, num_timesteps_out)
  predicting the average traffic speed using num_timesteps_in to predict the
  traffic conditions in the next num_timesteps_out
  
  Args:
      num_timesteps_in (int): number of timesteps the sequence model sees
      num_timesteps_out (int): number of timesteps the sequence model has to predict
docstr_tok: ["Uses","the","node","features","of","the","graph","and","generates","a","feature\/target","relationship","of","the","shape","(","num_nodes",",","num_node_features",",","num_timesteps_in",")","-",">","(","num_nodes",",","num_timesteps_out",")","predicting","the","average","traffic","speed","using","num_timesteps_in","to","predict","the","traffic","conditions","in","the","next","num_timesteps_out","Args",":","num_timesteps_in","(","int",")",":","number","of","timesteps","the","sequence","model","sees","num_timesteps_out","(","int",")",":","number","of","timesteps","the","sequence","model","has","to","predict"]
code: def _generate_task(
      self, num_timesteps_in: int = 12, num_timesteps_out: int = 12
  ):
      """Uses the node features of the graph and generates a feature/target
      relationship of the shape
      (num_nodes, num_node_features, num_timesteps_in) -> (num_nodes, num_timesteps_out)
      predicting the average traffic speed using num_timesteps_in to predict the
      traffic conditions in the next num_timesteps_out
  
      Args:
          num_timesteps_in (int): number of timesteps the sequence model sees
          num_timesteps_out (int): number of timesteps the sequence model has to predict
      """
      indices = [
          (i, i + (num_timesteps_in + num_timesteps_out))
          for i in range(
              self.X.shape[2]
              - (num_timesteps_in + num_timesteps_out)
              + 1
          )
      ]
  
      # Generate observations
      features, target = [], []
      for i, j in indices:
          features.append(
              (self.X[:, :, i : i + num_timesteps_in]).numpy()
          )
          target.append(
              (self.X[:, :, i + num_timesteps_in : j]).numpy()
          )
  
      self.features = features
      self.targets = target
  
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 5 for calling the PemsBayDatasetLoader obj's underlying member method code with example usage: obj._generate_task(num_timesteps_in, num_timesteps_out) without return types