text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from torch.autograd import Variable
  
  from net.utils.tgcn import ConvTemporalGraphical
  from net.utils.graph import Graph
  
  class Model(nn.Module):
      r"""Spatial temporal graph convolutional networks.
  
      Args:
          in_channels (int): Number of channels in the input data
          num_class (int): Number of classes for the classification task
          graph_args (dict): The arguments for building the graph
          edge_importance_weighting (bool): If ``True``, adds a learnable
              importance weighting to the edges of the graph
          **kwargs (optional): Other parameters for graph convolution units
  
      Shape:
          - Input: :math:`(N, in_channels, T_{in}, V_{in}, M_{in})`
          - Output: :math:`(N, num_class)` where
              :math:`N` is a batch size,
              :math:`T_{in}` is a length of input sequence,
              :math:`V_{in}` is the number of graph nodes,
              :math:`M_{in}` is the number of instance in a frame.
      """
  
      def __init__(self, in_channels, num_class, graph_args,
                   edge_importance_weighting, **kwargs):
          super().__init__()
  
          # load graph
          self.graph = Graph(**graph_args)
          A = torch.tensor(self.graph.A, dtype=torch.float32, requires_grad=False)
          self.register_buffer('A', A)
  
          # build networks
          spatial_kernel_size = A.size(0)
          temporal_kernel_size = 9
          kernel_size = (temporal_kernel_size, spatial_kernel_size)
          self.data_bn = nn.BatchNorm1d(in_channels * A.size(1))
          kwargs0 = {k: v for k, v in kwargs.items() if k != 'dropout'}
          self.st_gcn_networks = nn.ModuleList((
              st_gcn(in_channels, 64, kernel_size, 1, residual=False, **kwargs0),
              st_gcn(64, 64, kernel_size, 1, **kwargs),
              st_gcn(64, 64, kernel_size, 1, **kwargs),
              st_gcn(64, 64, kernel_size, 1, **kwargs),
              st_gcn(64, 128, kernel_size, 2, **kwargs),
              st_gcn(128, 128, kernel_size, 1, **kwargs),
              st_gcn(128, 128, kernel_size, 1, **kwargs),
              st_gcn(128, 256, kernel_size, 2, **kwargs),
              st_gcn(256, 256, kernel_size, 1, **kwargs),
              st_gcn(256, 256, kernel_size, 1, **kwargs),
          ))
  
          # initialize parameters for edge importance weighting
          if edge_importance_weighting:
              self.edge_importance = nn.ParameterList([
                  nn.Parameter(torch.ones(self.A.size()))
                  for i in self.st_gcn_networks
              ])
          else:
              self.edge_importance = [1] * len(self.st_gcn_networks)
  
          # fcn for prediction
          self.fcn = nn.Conv2d(256, num_class, kernel_size=1)
  
      def forward(self, x):
  
          # data normalization
          N, C, T, V, M = x.size()
          x = x.permute(0, 4, 3, 1, 2).contiguous()
          x = x.view(N * M, V * C, T)
          x = self.data_bn(x)
          x = x.view(N, M, V, C, T)
          x = x.permute(0, 1, 3, 4, 2).contiguous()
          x = x.view(N * M, C, T, V)
  
          # forwad
          for gcn, importance in zip(self.st_gcn_networks, self.edge_importance):
              x, _ = gcn(x, self.A * importance)
  
          # global pooling
          x = F.avg_pool2d(x, x.size()[2:])
          x = x.view(N, M, -1, 1, 1).mean(dim=1)
  
          # prediction
          x = self.fcn(x)
          x = x.view(x.size(0), -1)
  
          return x
  
      def extract_feature(self, x):
  
          # data normalization
          N, C, T, V, M = x.size()
          x = x.permute(0, 4, 3, 1, 2).contiguous()
          x = x.view(N * M, V * C, T)
          x = self.data_bn(x)
          x = x.view(N, M, V, C, T)
          x = x.permute(0, 1, 3, 4, 2).contiguous()
          x = x.view(N * M, C, T, V)
  
          # forwad
          for gcn, importance in zip(self.st_gcn_networks, self.edge_importance):
              x, _ = gcn(x, self.A * importance)
  
          _, c, t, v = x.size()
          feature = x.view(N, M, c, t, v).permute(0, 2, 3, 4, 1)
  
          # prediction
          x = F.avg_pool2d(x, x.size()[2:])
          x = x.view(N, M, -1, 1, 1).mean(dim=1)
          x = self.fcn(x)
          #output = x.view(N, M, -1, t, v).permute(0, 2, 3, 4, 1)
          output = x.view(x.size(0), -1)
          return output, feature
  
  class st_gcn(nn.Module):
      r"""Applies a spatial temporal graph convolution over an input graph sequence.
  
      Args:
          in_channels (int): Number of channels in the input sequence data
          out_channels (int): Number of channels produced by the convolution
          kernel_size (tuple): Size of the temporal convolving kernel and graph convolving kernel
          stride (int, optional): Stride of the temporal convolution. Default: 1
          dropout (int, optional): Dropout rate of the final output. Default: 0
          residual (bool, optional): If ``True``, applies a residual mechanism. Default: ``True``
  
      Shape:
          - Input[0]: Input graph sequence in :math:`(N, in_channels, T_{in}, V)` format
          - Input[1]: Input graph adjacency matrix in :math:`(K, V, V)` format
          - Output[0]: Outpu graph sequence in :math:`(N, out_channels, T_{out}, V)` format
          - Output[1]: Graph adjacency matrix for output data in :math:`(K, V, V)` format
  
          where
              :math:`N` is a batch size,
              :math:`K` is the spatial kernel size, as :math:`K == kernel_size[1]`,
              :math:`T_{in}/T_{out}` is a length of input/output sequence,
              :math:`V` is the number of graph nodes.
  
      """
  
      def __init__(self,
                   in_channels,
                   out_channels,
                   kernel_size,
                   stride=1,
                   dropout=0,
                   residual=True):
          super().__init__()
  
          assert len(kernel_size) == 2
          assert kernel_size[0] % 2 == 1
          padding = ((kernel_size[0] - 1) // 2, 0)
  
          self.gcn = ConvTemporalGraphical(in_channels, out_channels,
                                           kernel_size[1])
  
          self.tcn = nn.Sequential(
              nn.BatchNorm2d(out_channels),
              nn.ReLU(inplace=True),
              nn.Conv2d(
                  out_channels,
                  out_channels,
                  (kernel_size[0], 1),
                  (stride, 1),
                  padding,
              ),
              nn.BatchNorm2d(out_channels),
              nn.Dropout(dropout, inplace=True),
          )
  
          if not residual:
              self.residual = lambda x: 0
  
          elif (in_channels == out_channels) and (stride == 1):
              self.residual = lambda x: x
  
          else:
              self.residual = nn.Sequential(
                  nn.Conv2d(
                      in_channels,
                      out_channels,
                      kernel_size=1,
                      stride=(stride, 1)),
                  nn.BatchNorm2d(out_channels),
              )
  
          self.relu = nn.ReLU(inplace=True)
  
      def forward(self, x, A):
  
          res = self.residual(x)
          x, A = self.gcn(x, A)
          x = self.tcn(x) + res
  
          return self.relu(x), A
  