class_docstr: SwinUNet.
  Args:
      backbone: Backbone of SwinUnet
      image_size (int, optional): Image size of input. Default: 224
      path_size (int, optional): Patch size of Patch Embed. Default: 4
      in_chans (int, optional): The inchannel of input. Default: 3
      num_classes (int, optional): Number of class. Default: 1000
      embed_dim (int, optional): The embed dim. Default: 96
      depths (list, optional): Block number of every encoder layer. Default: [2, 2, 2, 2]
      depths_decoder (list, optional): Block number of every decoder layer. Default: [1, 2, 2, 2]
      num_heads (int, optional): Number of attention heads. Default: [3, 6, 12, 24]
      window_size (int, optional): Window size. Default: 7
      mlp_ratio (float, optional): Ratio of mlp hidden dim to embedding dim. Default: 4
      qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
      qk_scale (float | None, optional): Override default query and key scale of head_dim ** -0.5 if set. Default: None
      drop_rate (float, optional): Dropout rate. Default: 0.0
      attn_drop_rate (float, optional): Attention dropout rate. Default: 0.0
      drop_path_rate (float, optional): Stochastic depth rate. Default: 0.0
      norm_layer (nn.Layer, optional): Normalization layer.  Default: nn.LayerNorm
      patch_norm (bool, optional): If True, add normalization after patch embedding. Default: True
      pretrained(bool |str, optional): The path of pre-trained model. Default: None
      final_unsample (bool, optional): The final sample class. Default: True
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code: def forward_up_features(self, x, x_downsample):
      for inx, layer_up in enumerate(self.layers_up):
          if inx == 0:
              x = layer_up(x)
          else:
              x = paddle.concat([x, x_downsample[3 - inx]], -1)
              x = self.concat_back_dim[inx](x)
              x = layer_up(x)
  
      x = self.norm_up(x)  # B L C
  
      return x
  
imports: import numpy
  import paddle
  import paddle.nn
  from medicalseg.cvlibs import manager, param_init
  from medicalseg.models.backbones.swin_transformer import SwinTransformerBlock, window_partition
  from medicalseg.utils import load_pretrained_model
label_desc: Use image node_id 3 for calling the SwinUNet obj's underlying member method code with example usage: obj.forward_up_features(x, x_downsample) and returns: x