code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      # (N, L, C) --> (N, T, C)
      encoded = self.rnn(x)[0]
      # (N, C)
      return self.linear(encoded[:, -1, :])
  
imports: from copy import deepcopy
  from typing import Any, Callable, Dict, List, Optional, Tuple
  import torch
  from torch import nn
  from torch.nn import functional
  from torchvision.models._utils import IntermediateLayerGetter
  from doctr.datasets import VOCABS
  from ...classification import resnet31
  from ...utils.pytorch import _bf16_to_float32, load_pretrained_params
  from ..core import RecognitionModel, RecognitionPostProcessor
label_desc: Use image node_id 2 for calling the SAREncoder obj's underlying member method code with example usage: obj.forward(x) and returns: self