code: def forward(
      self,
      features: _typing.Sequence[torch.Tensor],
      *__args,
      **__kwargs,
  ) -> torch.Tensor:
      return torch.nn.functional.log_softmax(features[-1], dim=1)
  
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 1 for calling the _LogSoftmaxDecoder obj's underlying member method code with example usage: obj.forward(features) and returns: torch