code: def forward(self, x):
      output = {}
      if self.infer_mode:
          output["lr_img"] = x
          y = x
      else:
          output["lr_img"] = x[0]
          output["hr_img"] = x[1]
          y = x[0]
      if self.stn and self.training:
          _, ctrl_points_x = self.stn_head(y)
          y, _ = self.tps(y, ctrl_points_x)
      block = {"1": self.block1(y)}
      for i in range(self.srb_nums + 1):
          block[str(i + 2)] = getattr(self, "block%d" % (i + 2))(
              block[str(i + 1)]
          )
  
      block[str(self.srb_nums + 3)] = getattr(
          self, "block%d" % (self.srb_nums + 3)
      )((block["1"] + block[str(self.srb_nums + 2)]))
  
      sr_img = paddle.tanh(block[str(self.srb_nums + 3)])
  
      output["sr_img"] = sr_img
  
      if self.training:
          hr_img = x[1]
          length = x[2]
          input_tensor = x[3]
  
          # add transformer
          sr_pred, word_attention_map_pred, _ = self.r34_transformer(
              sr_img, length, input_tensor
          )
  
          hr_pred, word_attention_map_gt, _ = self.r34_transformer(
              hr_img, length, input_tensor
          )
  
          output["hr_img"] = hr_img
          output["hr_pred"] = hr_pred
          output["word_attention_map_gt"] = word_attention_map_gt
          output["sr_pred"] = sr_pred
          output["word_attention_map_pred"] = word_attention_map_pred
  
      return output
  
imports: import math
  import paddle
  import paddle.nn.functional
  from paddle import nn
  from collections import OrderedDict
  import sys
  import numpy
  import warnings
  import math, copy
  import cv2
  from .tps_spatial_transformer import TPSSpatialTransformer
  from .stn import STN
  from ppocr.modeling.heads.sr_rensnet_transformer import Transformer
label_desc: Use image node_id 2 for calling the TSRN obj's underlying member method code with example usage: obj.forward(x) and returns: output