code: def forward(self, features, mask, lengths, hidden_state):
      features = self.input_norm(features)
      if self.input_fc is not None:
          features = self.input_fc(features)
      features = self.embedding(features)
      features = self.tf(features, features, features, mask)
      add_after_pool = None
      if self.use_context:
          hidden_state = hidden_state.unsqueeze(1)
          ctx = self.tf_context(hidden_state, features, features, mask)
          add_after_pool = ctx.squeeze(1)
      pooled = self.pooler(features, mask, lengths)
      if add_after_pool is not None:
          pooled = torch.cat([pooled, add_after_pool], dim=-1)
      return pooled
  
imports: from collections import OrderedDict
  import os
  import numpy
  import torch
  from torch import nn
  from gluoncv.torch.utils.coot_utils import truncated_normal_fill
label_desc: Use image node_id 2 for calling the Transformer obj's underlying member method code with example usage: obj.forward(features, mask, lengths, hidden_state) and returns: pooled