code: def forward(
      self,
      features: _typing.Sequence[torch.Tensor],
      data: torch_geometric.data.Data,
      *__args,
      **__kwargs,
  ):
      x: torch.Tensor = features[-1]
      edge_index: torch.LongTensor = data.edge_index
      batch = data.batch
      if (
          self.num_graph_features is not None
          and isinstance(self.num_graph_features, int)
          and self.num_graph_features > 0
      ):
          if not (
              hasattr(data, "gf")
              and isinstance(data.gf, torch.Tensor)
              and data.gf.size() == (x.size(0), self.num_graph_features)
          ):
              raise ValueError(
                  f"The provided data is expected to contain property 'gf' "
                  f"with {self.num_graph_features} dimensions as graph feature"
              )
  
      x = torch.nn.functional.relu(self.conv1(x, edge_index))
      x, edge_index, _, batch, _, _ = self.pool1(
          x, edge_index, None, batch
      )
      x1 = torch.cat(
          [global_max_pool(x, batch), global_mean_pool(x, batch)], dim=1
      )
  
      x = torch.nn.functional.relu(self.conv2(x, edge_index))
      x, edge_index, _, batch, _, _ = self.pool2(
          x, edge_index, None, batch
      )
      x2 = torch.cat(
          [global_max_pool(x, batch), global_mean_pool(x, batch)], dim=1
      )
  
      x = torch.nn.functional.relu(self.conv3(x, edge_index))
      x, edge_index, _, batch, _, _ = self.pool3(
          x, edge_index, None, batch
      )
      x3 = torch.cat(
          [global_max_pool(x, batch), global_mean_pool(x, batch)], dim=1
      )
  
      x = x1 + x2 + x3
      if (
          isinstance(self.num_graph_features, int)
          and self.num_graph_features > 0
      ):
          x = torch.cat([x, data.gf], dim=-1)
      x = self.lin1(x)
      x = _utils.activation.activation_func(x, self._act)
      x = torch.nn.functional.dropout(
          x, p=self.dropout, training=self.training
      )
      x = self.lin2(x)
      x = _utils.activation.activation_func(x, self._act)
      x = torch.nn.functional.log_softmax(self.lin3(x), dim=-1)
      return x
  
imports: import torch.nn.functional
  import typing
  import torch_geometric
  from torch_geometric.nn.conv import GraphConv
  from torch_geometric.nn.pool import TopKPooling
  from torch_geometric.nn.glob import global_add_pool, global_max_pool, global_mean_pool
  from ...encoders import base_encoder
  from ..None import base_decoder, decoder_registry
  from ...None import _utils
label_desc: Use image node_id 2 for calling the _DiffPoolDecoder obj's underlying member method code with example usage: obj.forward(features, data) and returns: x