class_docstr: An implementation of the Multi-Component Spatial-Temporal Graph Convolution Networks, a degraded version of ASTGCN.
  For details see this paper: `"Attention Based Spatial-Temporal Graph Convolutional
  Networks for Traffic Flow Forecasting." <https://ojs.aaai.org/index.php/AAAI/article/view/3881>`_
  
  Args:
  
      nb_block (int): Number of ASTGCN blocks in the model.
      in_channels (int): Number of input features.
      K (int): Order of Chebyshev polynomials. Degree is K-1.
      nb_chev_filter (int): Number of Chebyshev filters.
      nb_time_filter (int): Number of time filters.
      time_strides (int): Time strides during temporal convolution.
      num_for_predict (int): Number of predictions to make in the future.
      len_input (int): Length of the input sequence.
code: def __init__(
      self,
      nb_block: int,
      in_channels: int,
      K: int,
      nb_chev_filter: int,
      nb_time_filter: int,
      time_strides: int,
      num_for_predict: int,
      len_input: int,
  ):
      super(MSTGCN, self).__init__()
  
      self._blocklist = nn.ModuleList(
          [
              MSTGCNBlock(
                  in_channels,
                  K,
                  nb_chev_filter,
                  nb_time_filter,
                  time_strides,
              )
          ]
      )
  
      self._blocklist.extend(
          [
              MSTGCNBlock(
                  nb_time_filter, K, nb_chev_filter, nb_time_filter, 1
              )
              for _ in range(nb_block - 1)
          ]
      )
  
      self._final_conv = nn.Conv2d(
          int(len_input / time_strides),
          num_for_predict,
          kernel_size=(1, nb_time_filter),
      )
  
      self._reset_parameters()
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/nn/attention/mstgcn.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch_geometric.data import Data
  from torch_geometric.nn import ChebConv
  from torch_geometric.transforms import LaplacianLambdaMax
label_desc: Use image node_id 1 to create a new MSTGCN object from inherited base classes: nn with example: obj = MSTGCN(nb_block, in_channels, K, nb_chev_filter, nb_time_filter, time_strides, num_for_predict, len_input)