code: def forward(self, input_feature):
      recurrent, _ = self.rnn(
          input_feature
      )  # batch_size x T x input_size -> batch_size x T x (2*hidden_size)
      if self.with_linear:
          output = self.linear(
              recurrent
          )  # batch_size x T x output_size
          return output
      return recurrent
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle import nn
  from ppocr.modeling.heads.rec_ctc_head import get_para_bias_attr
  from ppocr.modeling.backbones.rec_svtrnet import Block, ConvBNLayer, trunc_normal_, zeros_, ones_
label_desc: Use image node_id 2 for calling the BidirectionalLSTM obj's underlying member method code with example usage: obj.forward(input_feature) and returns: recurrent, output