code: def forward(
      self, feed_dict, return_graph=False, return_pretrain=True
  ):
      h_nodes = []
      # adding pretrain relevant feature information
      bond_angle_index = feed_dict["bond_angle_index"]
      bond_angle = feed_dict["bond_angle"]
      bond_angle_mask = feed_dict["bond_angle_mask"]
      edge_index = feed_dict["edge_index"]
      edge_attr_float = feed_dict["edge_attr_float"]
      edge_attr_float_mask = feed_dict["edge_attr_float_mask"]
      tid = feed_dict["tid"]
  
      for i in range(self.config.block_num):
          h_nodes.append(self.gnn_blocks[i](feed_dict))
      h_node = paddle.concat(h_nodes, axis=-1)
  
      pretrain_losses = {}
      if "Con" in self.pretrain_tasks:
          pred2 = self.contextmlp(h_node)
          #             print(f"pred2 shape :{pred2.shape}, tid shape: {tid.shape}")
          context_loss = self.context_loss(pred2, tid)
          pretrain_losses["Con"] = context_loss
      if "Ba" in self.pretrain_tasks:
          pretrain_losses["Ba"] = self.pretrain_bond_angle(
              h_node, bond_angle_index, bond_angle, bond_angle_mask
          )
      if "Bl" in self.pretrain_tasks:
          pretrain_losses["Bl"] = self.pretrain_bond_length(
              h_node, edge_index, edge_attr_float, edge_attr_float_mask
          )
  
      if self.graph_pooling == "bisop":
          h_graph = self.pool(h_node)
      else:
          h_graph = self.pool(feed_dict["graph"], h_node)
      output = self.graph_pred_linear(h_graph)
  
      #  if self.training:
      #      return output
      #  else:
      #      # At inference time, relu is applied to output to ensure positivity
      #      return paddle.clip(output, min=0, max=50)
      # return output
      if return_graph and return_pretrain:
          return output, pretrain_losses
      if return_pretrain:
          return pretrain_losses
      if return_graph:
          return output
  
imports: import os
  import sys
  import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import pgl.nn
  from pgl.utils.logger import log
  import models.conv
  import models.mol_encoder
  import models.layers
label_desc: Use image node_id 2 for calling the GNN obj's underlying member method code with example usage: obj.forward(feed_dict, return_graph, return_pretrain) and returns: output,  pretrain_losses, pretrain_losses, output