docstr: text : (batch, seq_len, embedding_size)
  global_info: (batch, embedding_size, 1, 1)
  conv_feature: (batch, channel, H, W)
docstr_tok: ["text",":","(","batch",",","seq_len",",","embedding_size",")","global_info",":","(","batch",",","embedding_size",",","1",",","1",")","conv_feature",":","(","batch",",","channel",",","H",",","W",")"]
code: def forward(self, conv_feature):
      """
      text : (batch, seq_len, embedding_size)
      global_info: (batch, embedding_size, 1, 1)
      conv_feature: (batch, channel, H, W)
      """
      batch = paddle.shape(conv_feature)[0]
      position2d = (
          positionalencoding2d(64, 16, 64)
          .cast("float32")
          .unsqueeze(0)
          .reshape([1, 64, 1024])
      )
      position2d = position2d.tile([batch, 1, 1])
      conv_feature = paddle.concat(
          [conv_feature, position2d], 1
      )  # batch, 128(64+64), 32, 128
      result = conv_feature.transpose([0, 2, 1])
      origin_result = result
      result = self.mul_layernorm1(
          origin_result
          + self.multihead(result, result, result, mask=None)[0]
      )
      origin_result = result
      result = self.mul_layernorm3(origin_result + self.pff(result))
      result = self.linear(result)
      return result.transpose([0, 2, 1])
  
imports: import math
  import warnings
  import numpy
  import paddle
  from paddle import nn
  import string
  from .tps_spatial_transformer import TPSSpatialTransformer
  from .stn import STN
  from .tsrn import GruBlock, mish, UpsampleBLock
  from ppocr.modeling.heads.sr_rensnet_transformer import Transformer, LayerNorm, PositionwiseFeedForward, MultiHeadedAttention
label_desc: Use image node_id 2 for calling the FeatureEnhancer obj's underlying member method code with example usage: obj.forward(conv_feature) and returns: result