code: def forward(self, hidden_states):
      hidden_states = self.token_norm(hidden_states)
      hidden_states = hidden_states.transpose(1, 2).unsqueeze(-1)
      selected = self.tokenLearner(hidden_states)
      selected = selected.flatten(2)
      attentions = F.softmax(selected, dim=-1)
  
      feat = self.feat(hidden_states)
      feat = feat.flatten(2).transpose(1, 2)
      feat = torch.einsum("...si,...id->...sd", attentions, feat)
      a3_out = self.norm(feat)
  
      return (a3_out, attentions)
  
imports: import collections.abc
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch import nn
  from ...modeling_outputs import BaseModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_mgp_str import MgpstrConfig
label_desc: Use image node_id 2 for calling the MgpstrA3Module obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: a3_out,  attentions