class_docstr: Graph 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.
  Temporal attention, spatial attention and channel-wise attention will be applied.
  Args:
      in_channels (int): Number of input features.
      out_channels (int): Number of output features.
      A (Tensor array): Adaptive Graph.
      coff_embedding (int, optional): Coefficient Embeddings. (default: :int:`4`)
      num_subset (int, optional): Subsets for adaptive graphs, see
      :math:`\mathbf{A}, \mathbf{B}, \mathbf{C}` in https://arxiv.org/abs/1805.07694
      for details. (default: :int:`3`)
      adaptive (bool, optional): Apply Adaptive Graph Convolutions. (default: :obj:`True`)
      attention (bool, optional): Apply Attention. (default: :obj:`True`)
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code: def _init_conv_bn(self):
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              self._conv_init(m)
          elif isinstance(m, nn.BatchNorm2d):
              self._bn_init(m, 1)
      self._bn_init(self.bn, 1e-6)
  
      for i in range(self.num_subset):
          self._conv_branch_init(self.conv_d[i], self.num_subset)
  
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 5 for calling the UnitGCN obj's underlying member method code with example usage: obj._init_conv_bn() without return types