class_docstr: The implementation of segmenter with mask head based on PaddlePaddle.
  
  The original article refers to Strudel, Robin, et al. "Segmenter: Transformer
  for Semantic Segmentation." arXiv preprint arXiv:2105.05633 (2021).
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (nn.Layer): The backbone transformer network.
      h_embed_dim (int): The embedding dim in mask head.
      h_depth (int): The num of layers in mask head.
      h_num_heads (int): The num of heads of MSA in mask head.
      h_mlp_ratio (int, optional): Ratio of MLP dim in mask head. Default: 4.
      h_drop_rate (float, optional): Drop rate of MLP in mask head. Default: 0.0.
      h_drop_path_rate (float, optional): Drop path rate in mask head. Default: 0.0.
      h_attn_drop_rate (float, optional): Attenation drop rate in mask head. Default: 0.0.
      h_qkv_bias (bool, optional): Whether add bias in mask head. Default: False.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
code: def __init__(
      self,
      num_classes,
      backbone,
      h_embed_dim,
      h_depth,
      h_num_heads,
      h_mlp_ratio=4,
      h_drop_rate=0.0,
      h_drop_path_rate=0.0,
      h_attn_drop_rate=0.0,
      h_qkv_bias=False,
      pretrained=None,
  ):
      super().__init__()
      self.backbone = backbone
      self.head = SegmenterMaskHead(
          num_classes,
          backbone.embed_dim,
          h_embed_dim,
          h_depth,
          h_num_heads,
          h_mlp_ratio,
          h_drop_rate,
          h_drop_path_rate,
          h_attn_drop_rate,
          h_qkv_bias,
      )
      self.pretrained = pretrained
      self.init_weight()
  
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 1 to create a new MaskSegmenter object from inherited base classes: nn with example: obj = MaskSegmenter(num_classes, backbone, h_embed_dim, h_depth, h_num_heads, h_mlp_ratio, h_drop_rate, h_drop_path_rate, h_attn_drop_rate, h_qkv_bias, pretrained)