code: def attention(self, output_encoder, hidden_decoder, attention_mask):
      enc_hiddens_proj = self.att_projection(output_encoder)
      e_t = torch.bmm(
          enc_hiddens_proj, hidden_decoder.unsqueeze(-1)
      ).squeeze(-1)
      e_t.data.masked_fill_(attention_mask == 0, -float("inf"))
      alpha_t = F.softmax(e_t, dim=-1)
      a_t = torch.bmm(alpha_t.unsqueeze(1), output_encoder).squeeze(1)
      return a_t
  
imports: import inspect
  import warnings
  from typing import Optional, Tuple, Dict, Any
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers.utils import ModelOutput
  from transformers.modeling_utils import PreTrainedModel
  from transformers.configuration_utils import PretrainedConfig
  from torch.nn import CrossEntropyLoss
  from .abstract_model import AbstractModel
label_desc: Use image node_id 7 for calling the RNNSeq2Seq obj's underlying member method code with example usage: obj.attention(output_encoder, hidden_decoder, attention_mask) and returns: a_t