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.
docstr: Resetting the model parameters.
docstr_tok: ["Resetting","the","model","parameters","."]
code: def _reset_parameters(self):
      """
      Resetting the model parameters.
      """
      for p in self.parameters():
          if p.dim() > 1:
              nn.init.xavier_uniform_(p)
          else:
              nn.init.uniform_(p)
  
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 2 for calling the MSTGCN obj's underlying member method code with example usage: obj._reset_parameters() without return types