code: def forward(self, src, mask, query_embed, pos_embed):
      # flatten NxCxHxW to HWxNxC
      bs, c, h, w = src.shape
      src = paddle.transpose(
          paddle.flatten(src, start_axis=2), (2, 0, 1)
      )
      pos_embed = paddle.transpose(
          paddle.flatten(pos_embed, start_axis=2), (2, 0, 1)
      )
      query_embed = paddle.stack(
          [query_embed for i in range(bs)], axis=1
      )
      if mask is not None:
          mask = paddle.flatten(mask, start_axis=1)
  
      tgt = paddle.zeros_like(
          query_embed
      )  # No.querry, N, hdim [100, 2, 256]
      memory = self.encoder(
          src, src_key_padding_mask=mask, pos=pos_embed
      )  # HWxNxC memory = src
      hs = self.decoder(
          tgt,
          memory,
          memory_key_padding_mask=mask,
          pos=pos_embed,
          query_pos=query_embed,
      )
  
      return paddle.transpose(hs, (0, 2, 1, 3)), paddle.reshape(
          paddle.transpose(memory, (1, 2, 0)), (bs, c, h, w)
      )
  
imports: import math
  import copy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.utils import utils
  from paddleseg.core.train import check_logits_losses
label_desc: Use image node_id 3 for calling the Transformer obj's underlying member method code with example usage: obj.forward(src, mask, query_embed, pos_embed) and returns: paddle,  paddle