code: def __init__(
      self,
      in_channels,
      num_classes,
      hidden_dim,
      num_queries,
      num_heads,
      ff_dim,
      num_dec_layers,
      pre_norm,
      mask_dim,
      enforce_input_proj,
  ):
      super().__init__()
  
      # Positional encoding
      n_steps = hidden_dim // 2
      self.pe_layer = PositionEmbeddingSine(n_steps, normalize=True)
  
      # Define Transformer decoder here
      self.num_heads = num_heads
      self.num_layers = num_dec_layers
      self.transformer_self_attention_layers = nn.LayerList()
      self.transformer_cross_attention_layers = nn.LayerList()
      self.transformer_ffn_layers = nn.LayerList()
  
      for _ in range(self.num_layers):
          self.transformer_self_attention_layers.append(
              SelfAttentionLayer(
                  embed_dim=hidden_dim,
                  num_heads=num_heads,
                  dropout=0.0,
                  normalize_before=pre_norm,
              )
          )
  
          self.transformer_cross_attention_layers.append(
              CrossAttentionLayer(
                  embed_dim=hidden_dim,
                  num_heads=num_heads,
                  dropout=0.0,
                  normalize_before=pre_norm,
              )
          )
  
          self.transformer_ffn_layers.append(
              FFNLayer(
                  embed_dim=hidden_dim,
                  ff_dim=ff_dim,
                  dropout=0.0,
                  normalize_before=pre_norm,
              )
          )
  
      self.decoder_norm = nn.LayerNorm(hidden_dim)
  
      self.num_queries = num_queries
      # Learnable query features
      self.query_feat = nn.Embedding(num_queries, hidden_dim)
      # Learnable query
      self.query_embed = nn.Embedding(num_queries, hidden_dim)
  
      # Level embedding (we always use 3 scales)
      self.num_feature_levels = 3
      self.level_embed = nn.Embedding(
          self.num_feature_levels, hidden_dim
      )
      self.input_proj = nn.LayerList()
      for _ in range(self.num_feature_levels):
          if in_channels != hidden_dim or enforce_input_proj:
              self.input_proj.append(
                  Conv2D(in_channels, hidden_dim, kernel_size=1)
              )
              c2_xavier_fill(self.input_proj[-1])
          else:
              self.input_proj.append(nn.Sequential())
  
      # Output FFNs
      self.class_embed = nn.Linear(hidden_dim, num_classes + 1)
      self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)
  
      self.init_weight()
  
imports: 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, th_linear_fill, th_multihead_fill
  from .common import Conv2D, PositionEmbeddingSine
label_desc: Use image node_id 1 to create a new MultiScaleMaskedTransformerDecoder object from inherited base classes: nn with example: obj = MultiScaleMaskedTransformerDecoder(in_channels, num_classes, hidden_dim, num_queries, num_heads, ff_dim, num_dec_layers, pre_norm, mask_dim, enforce_input_proj)