class_docstr: Implementation of Spatio-Temporal Graph Convolutional Networks
docstr: Output layer
docstr_tok: ["Output","layer"]
code: def output_layer(self, x, T, name, act_func="GLU"):
      """Output layer"""
      _, _, n, channel = x.shape
  
      # maps multi-steps to one.
      x_i = self.temporal_conv_layer(
          x=x,
          Kt=T,
          c_in=channel,
          c_out=channel,
          name="%s_in" % name,
          act_func=act_func,
      )
      x_ln = fl.layer_norm(x_i)
      x_o = self.temporal_conv_layer(
          x=x_ln,
          Kt=1,
          c_in=channel,
          c_out=channel,
          name="%s_out" % name,
          act_func="sigmoid",
      )
  
      # maps multi-channels to one.
      x_fc = self.fully_con_layer(
          x=x_o, n=n, channel=channel, name="%s_fc" % name
      )
      return x_fc
  
file_path: PGL/legacy/examples/stgcn/models/model.py
imports: import numpy
  import paddle.fluid
  import paddle.fluid.layers
  import pgl
label_desc: Use image node_id 7 for calling the STGCNModel obj's underlying member method code with example usage: obj.output_layer(x, T, name, act_func) and returns: x_fc