class_docstr: Implementation of Spatio-Temporal Graph Convolutional Networks
docstr: Fully connected layer
docstr_tok: ["Fully","connected","layer"]
code: def fully_con_layer(self, x, n, channel, name):
      """Fully connected layer"""
      bt_init = fluid.initializer.ConstantInitializer(value=0.0)
      bt = fl.create_parameter(
          shape=[n, 1],
          dtype="float32",
          attr=fluid.ParamAttr(
              name="%s_bt" % name, trainable=True, initializer=bt_init
          ),
      )
      x_conv = fl.conv2d(
          input=x,
          num_filters=1,
          filter_size=[1, 1],
          stride=[1, 1],
          padding="SAME",
          data_format="NHWC",
          param_attr=fluid.ParamAttr(name="%s_conv2d" % name),
      )
      x_conv = x_conv + bt
      return x_conv
  
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 8 for calling the STGCNModel obj's underlying member method code with example usage: obj.fully_con_layer(x, n, channel, name) and returns: x_conv