class_docstr: The linear head of Segmenter.
  Args:
      num_classes (int): The unique number of target classes.
      in_dim (int): The embed dim of input.
docstr: Forward function.
  Args:
      x (Tensor): Input tensor of decoder.
      patch_embed_size (Tensor): The height and width of the patch embed tensor.
  Returns:
      list[Tensor]: Segmentation results.
docstr_tok: ["Forward","function",".","Args",":","x","(","Tensor",")",":","Input","tensor","of","decoder",".","patch_embed_size","(","Tensor",")",":","The","height","and","width","of","the","patch","embed","tensor",".","Returns",":","list","[","Tensor","]",":","Segmentation","results","."]
code: def forward(self, x, patch_embed_size):
      """Forward function.
      Args:
          x (Tensor): Input tensor of decoder.
          patch_embed_size (Tensor): The height and width of the patch embed tensor.
      Returns:
          list[Tensor]: Segmentation results.
      """
      masks = self.head(x)
  
      # [b, (h w), c] -> [b, c, h, w]
      h, w = patch_embed_size[0], patch_embed_size[1]
      masks = masks.reshape((0, h, w, paddle.shape(masks)[-1]))
      masks = masks.transpose((0, 3, 1, 2))
  
      return [masks]
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  from paddleseg.utils import utils
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models.backbones import vision_transformer, transformer_utils
label_desc: Use image node_id 2 for calling the SegmenterLinearHead obj's underlying member method code with example usage: obj.forward(x, patch_embed_size) and returns: list