class_docstr: An implementation of the Attention Based Spatial-Temporal Graph Convolutional Block.
  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:
      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_of_vertices (int): Number of vertices in the graph.
      num_of_timesteps (int): Number of time lags.
      normalization (str, optional): The normalization scheme for the graph
          Laplacian (default: :obj:`"sym"`):
          1. :obj:`None`: No normalization
          :math:`\mathbf{L} = \mathbf{D} - \mathbf{A}`
          2. :obj:`"sym"`: Symmetric normalization
          :math:`\mathbf{L} = \mathbf{I} - \mathbf{D}^{-1/2} \mathbf{A}
          \mathbf{D}^{-1/2}`
          3. :obj:`"rw"`: Random-walk normalization
          :math:`\mathbf{L} = \mathbf{I} - \mathbf{D}^{-1} \mathbf{A}`
          You need to pass :obj:`lambda_max` to the :meth:`forward` method of
          this operator in case the normalization is non-symmetric.
          :obj:`\lambda_max` should be a :class:`torch.Tensor` of size
          :obj:`[num_graphs]` in a mini-batch scenario and a
          scalar/zero-dimensional tensor when operating on single graphs.
          You can pre-compute :obj:`lambda_max` via the
          :class:`torch_geometric.transforms.LaplacianLambdaMax` transform.
      bias (bool, optional): If set to :obj:`False`, the layer will not learn
          an additive bias. (default: :obj:`True`)
code: def _reset_parameters(self):
      for p in self.parameters():
          if p.dim() > 1:
              nn.init.xavier_uniform_(p)
          else:
              nn.init.uniform_(p)
  
imports: import math
  from typing import Optional, List, Union
  import torch
  import torch.nn
  from torch.nn import Parameter
  import torch.nn.functional
  from torch_geometric.data import Data
  from torch_geometric.typing import OptTensor
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.transforms import LaplacianLambdaMax
  from torch_geometric.utils import remove_self_loops, add_self_loops, get_laplacian
label_desc: Use image node_id 2 for calling the ASTGCNBlock obj's underlying member method code with example usage: obj._reset_parameters() without return types