code: def get_gat_layer(
      self,
      i,
      gw,
      feature,
      hidden_size,
      num_heads,
      concat=True,
      layer_norm=True,
      relu=True,
      gate=False,
      edge_feature=None,
  ):
      fan_in = feature.shape[-1]
      bias_bound = 1.0 / math.sqrt(fan_in)
      fc_bias_attr = F.ParamAttr(
          initializer=F.initializer.UniformInitializer(
              low=-bias_bound, high=bias_bound
          )
      )
  
      negative_slope = math.sqrt(5)
      gain = math.sqrt(2.0 / (1 + negative_slope**2))
      std = gain / math.sqrt(fan_in)
      weight_bound = math.sqrt(3.0) * std
      fc_w_attr = F.ParamAttr(
          initializer=F.initializer.UniformInitializer(
              low=-weight_bound, high=weight_bound
          )
      )
  
      if concat:
          skip_feature = L.fc(
              feature,
              hidden_size * num_heads,
              param_attr=fc_w_attr,
              name="fc_skip_" + str(i),
              bias_attr=fc_bias_attr,
          )
      else:
          skip_feature = L.fc(
              feature,
              hidden_size,
              param_attr=fc_w_attr,
              name="fc_skip_" + str(i),
              bias_attr=fc_bias_attr,
          )
      out_feat = transformer_gat_pgl(
          gw,
          feature,
          hidden_size,
          "gat_" + str(i),
          num_heads,
          concat=concat,
          edge_feature=edge_feature,
      )
      #         out_feat= out_feat + skip_feature
  
      if gate:
          fan_in = out_feat.shape[-1] * 3
          bias_bound = 1.0 / math.sqrt(fan_in)
          fc_bias_attr = F.ParamAttr(
              initializer=F.initializer.UniformInitializer(
                  low=-bias_bound, high=bias_bound
              )
          )
  
          negative_slope = math.sqrt(5)
          gain = math.sqrt(2.0 / (1 + negative_slope**2))
          std = gain / math.sqrt(fan_in)
          weight_bound = math.sqrt(3.0) * std
          fc_w_attr = F.ParamAttr(
              initializer=F.initializer.UniformInitializer(
                  low=-weight_bound, high=weight_bound
              )
          )
  
          gate_f = L.fc(
              [skip_feature, out_feat, out_feat - skip_feature],
              1,
              param_attr=fc_w_attr,
              name="gate_" + str(i),
              bias_attr=fc_bias_attr,
          )
  
          gate_f = L.sigmoid(gate_f)
  
          out_feat = skip_feature * gate_f + out_feat * (1 - gate_f)
  
      else:
          out_feat = out_feat + skip_feature
  
      if layer_norm:
          lay_norm_attr = F.ParamAttr(
              initializer=F.initializer.ConstantInitializer(value=1)
          )
          lay_norm_bias = F.ParamAttr(
              initializer=F.initializer.ConstantInitializer(value=0)
          )
          out_feat = L.layer_norm(
              out_feat,
              name="layer_norm_" + str(i),
              param_attr=lay_norm_attr,
              bias_attr=lay_norm_bias,
          )
      if relu:
          out_feat = L.relu(out_feat)
      return out_feat
  
imports: import math
  import paddle.fluid
  import paddle.fluid.layers
  from module.transformer_gat_pgl import transformer_gat_pgl
label_desc: Use image node_id 3 for calling the Proteins_baseline_model obj's underlying member method code with example usage: obj.get_gat_layer(i, gw, feature, hidden_size, num_heads, concat, layer_norm, relu, gate, edge_feature) and returns: out_feat