code: def forward(self, x, mask=None):
      if mask is None:
          mask = paddle.zeros(
              shape=(x.shape[0], x.shape[2], x.shape[3]), dtype="bool"
          )
      not_mask = ~mask
      y_embed = paddle.cumsum(not_mask, axis=1, dtype="float32")
      x_embed = paddle.cumsum(not_mask, axis=2, dtype="float32")
  
      if self.normalize:
          y_embed = y_embed / (y_embed[:, -1:, :] + 1e-6) * self.scale
          x_embed = x_embed / (x_embed[:, :, -1:] + 1e-6) * self.scale
  
      dim_t = paddle.arange(self.num_pos_feats, dtype="float32")
      dim_t = paddle.cast(dim_t, dtype="int64")
      tmp = paddle.ones_like(dim_t) * 2
      dim_t = self.temperature ** (
          2 * paddle.floor_divide(dim_t, tmp) / self.num_pos_feats
      )
      pos_x = x_embed[:, :, :, None] / dim_t
      pos_y = y_embed[:, :, :, None] / dim_t
  
      pos_x = paddle.flatten(
          paddle.stack(
              (
                  paddle.sin(pos_x[:, :, :, 0::2]),
                  paddle.cos(pos_x[:, :, :, 1::2]),
              ),
              axis=4,
          ),
          start_axis=3,
      )
      pos_y = paddle.flatten(
          paddle.stack(
              (
                  paddle.sin(pos_y[:, :, :, 0::2]),
                  paddle.cos(pos_y[:, :, :, 1::2]),
              ),
              axis=4,
          ),
          start_axis=3,
      )
      pos = paddle.transpose(
          paddle.concat((pos_y, pos_x), axis=3), perm=(0, 3, 1, 2)
      )
      return pos
  
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 2 for calling the PositionEmbeddingSine obj's underlying member method code with example usage: obj.forward(x, mask) and returns: pos