class_docstr: Temporal Convolutional Block applied to nodes in the Two-Stream Adaptive Graph
  Convolutional Network as originally implemented in the
  `Github Repo <https://github.com/lshiwjx/2s-AGCN>`. For implementational details
  see https://arxiv.org/abs/1805.07694
  Args:
      in_channels (int): Number of input features.
      out_channels (int): Number of output features.
      kernel_size (int): Convolutional kernel size. (default: :obj:`9`)
      stride (int): Temporal Convolutional kernel stride. (default: :obj:`1`)
class_docstr_tok: ["Temporal","Convolutional","Block","applied","to","nodes","in","the","Two-Stream","Adaptive","Graph","Convolutional","Network","as","originally","implemented","in","the","`","Github","Repo","<","https",":","\/\/github.com\/lshiwjx\/2s-AGCN",">","`",".","For","implementational","details","see","https",":","\/\/arxiv.org\/abs\/1805.07694","Args",":","in_channels","(","int",")",":","Number","of","input","features",".","out_channels","(","int",")",":","Number","of","output","features",".","kernel_size","(","int",")",":","Convolutional","kernel","size",".","(","default",":",":","obj",":","`","9","`",")","stride","(","int",")",":","Temporal","Convolutional","kernel","stride",".","(","default",":",":","obj",":","`","1","`",")"]
code: def _bn_init(self, bn, scale):
      nn.init.constant_(bn.weight, scale)
      nn.init.constant_(bn.bias, 0)
  
imports: import math
  import torch
  import numpy
  import torch.nn
  from torch.autograd import Variable
  from torch_geometric.utils.to_dense_adj import to_dense_adj
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the UnitTCN obj's underlying member method code with example usage: obj._bn_init(bn, scale) without return types