code: def forward(self, features):
      srcs = []
      pos = []
      # Reverse feature maps into top-down order (from low to high resolution)
      for idx, ti in enumerate(self.transformer_indices[::-1]):
          x = features[ti].astype(
              "float32"
          )  # deformable detr does not support half precision
          srcs.append(self.input_proj[idx](x))
          pos.append(self.pe_layer(x))
  
      y, spatial_shapes, level_start_index = self.transformer(srcs, pos)
      bs = y.shape[0]
  
      split_size_or_sections = [None] * self.num_feature_levels
      for i in range(self.num_feature_levels):
          if i < self.num_feature_levels - 1:
              split_size_or_sections[i] = (
                  level_start_index[i + 1] - level_start_index[i]
              )
          else:
              split_size_or_sections[i] = (
                  y.shape[1] - level_start_index[i]
              )
      y = paddle.split(y, split_size_or_sections, axis=1)
  
      out = []
      multi_scale_features = []
      num_cur_levels = 0
      for i, z in enumerate(y):
          out.append(
              z.transpose((0, 2, 1)).reshape(
                  (bs, -1, spatial_shapes[i][0], spatial_shapes[i][1])
              )
          )
  
      # Append `out` with extra FPN levels
      for idx, x in enumerate(features[: self.num_fpn_levels][::-1]):
          x = x.astype("float32")
          lateral_conv = self.lateral_convs[idx]
          output_conv = self.output_convs[idx]
          cur_fpn = lateral_conv(x)
          # Following FPN implementation, we use nearest upsampling here
          y = cur_fpn + F.interpolate(
              out[-1],
              size=cur_fpn.shape[-2:],
              mode="bilinear",
              align_corners=False,
          )
          y = output_conv(y)
          out.append(y)
  
      for o in out:
          if num_cur_levels < self.maskformer_num_feature_levels:
              multi_scale_features.append(o)
              num_cur_levels += 1
  
      return multi_scale_features, self.mask_conv(out[-1])
  
imports: import copy
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import param_init
  from paddlepanseg.models.param_init import c2_xavier_fill, THLinearInitMixin
  from .common import Conv2D, PositionEmbeddingSine
  from ._ms_deform_attn import MSDeformAttn
label_desc: Use image node_id 2 for calling the MSDeformAttnPixelDecoder obj's underlying member method code with example usage: obj.forward(features) and returns: multi_scale_features,  self