docstr: Predicts masks given an image and prompt embeddings, using a
  tranformer architecture.
  
  Arguments:
    transformer_dim (int): the channel dimension of the transformer
    transformer (nn.Module): the transformer used to predict masks
    num_multimask_outputs (int): the number of masks to predict
      when disambiguating masks
    activation (nn.Module): the type of activation to use when
      upscaling masks
    iou_head_depth (int): the depth of the MLP used to predict
      mask quality
    iou_head_hidden_dim (int): the hidden dimension of the MLP
      used to predict mask quality
code: def __init__(
      self,
      *,
      transformer_dim: int,
      transformer: paddle.nn.Layer,
      num_multimask_outputs: int = 3,
      activation: Type[paddle.nn.Layer] = paddle.nn.GELU,
      iou_head_depth: int = 3,
      iou_head_hidden_dim: int = 256,
  ) -> None:
      """
      Predicts masks given an image and prompt embeddings, using a
      tranformer architecture.
  
      Arguments:
        transformer_dim (int): the channel dimension of the transformer
        transformer (nn.Module): the transformer used to predict masks
        num_multimask_outputs (int): the number of masks to predict
          when disambiguating masks
        activation (nn.Module): the type of activation to use when
          upscaling masks
        iou_head_depth (int): the depth of the MLP used to predict
          mask quality
        iou_head_hidden_dim (int): the hidden dimension of the MLP
          used to predict mask quality
      """
      super().__init__()
      self.transformer_dim = transformer_dim
      self.transformer = transformer
      self.num_multimask_outputs = num_multimask_outputs
      self.iou_token = paddle.nn.Embedding(1, transformer_dim)
      self.num_mask_tokens = num_multimask_outputs + 1
      self.mask_tokens = paddle.nn.Embedding(
          self.num_mask_tokens, transformer_dim
      )
      self.output_upscaling = paddle.nn.Sequential(
          paddle.nn.Conv2DTranspose(
              in_channels=transformer_dim,
              out_channels=transformer_dim // 4,
              kernel_size=2,
              stride=2,
          ),
          LayerNorm2d(transformer_dim // 4),
          activation(),
          paddle.nn.Conv2DTranspose(
              in_channels=transformer_dim // 4,
              out_channels=transformer_dim // 8,
              kernel_size=2,
              stride=2,
          ),
          activation(),
      )
      self.output_hypernetworks_mlps = paddle.nn.LayerList(
          sublayers=[
              MLP(
                  transformer_dim,
                  transformer_dim,
                  transformer_dim // 8,
                  3,
              )
              for i in range(self.num_mask_tokens)
          ]
      )
      self.iou_prediction_head = MLP(
          transformer_dim,
          iou_head_hidden_dim,
          self.num_mask_tokens,
          iou_head_depth,
      )
  
file_path: PaddleSeg/contrib/SegmentAnything/segment_anything/modeling/mask_decoder.py
imports: import paddle
  from typing import List, Tuple, Type
  from common import LayerNorm2d
label_desc: Use image node_id 1 to create a new MaskDecoder object from inherited base classes: paddle.nn with example: obj = MaskDecoder()