code: def __init__(
      self,
      in_channels,
      mask_classification,
      num_classes=150,
      hidden_dim=256,
      num_queries=100,
      nheads=8,
      dropout=0.1,
      dim_feedforward=2048,
      enc_layers=0,
      dec_layers=6,
      pre_norm=False,
      deep_supervision=True,
      mask_dim=256,
      enforce_input_project=False,
  ):
      super().__init__()
      self.mask_classification = mask_classification
      self.pe_layer = PositionEmbeddingSine(
          hidden_dim // 2, normalize=True
      )
  
      self.transformer = Transformer(
          d_model=hidden_dim,
          dropout=dropout,
          nhead=nheads,
          dim_feedforward=dim_feedforward,
          num_encoder_layers=enc_layers,
          num_decoder_layers=dec_layers,
          normalize_before=pre_norm,
          return_intermediate_dec=deep_supervision,
      )
  
      self.query_embed = nn.Embedding(num_queries, hidden_dim)
  
      if in_channels != hidden_dim or enforce_input_project:
          self.input_proj = nn.Conv2D(
              in_channels, hidden_dim, kernel_size=1
          )
          if hasattr(self.input_proj, "weight"):
              param_init.kaiming_uniform(
                  self.input_proj.weight,
                  negative_slope=1,
                  nonlinearity="leaky_relu",
              )
          if getattr(self.input_proj, "bias", None) is not None:
              param_init.constant_init(self.input_proj.bias, value=0)
      else:
          self.input_proj = nn.Sequential()
  
      self.aux_loss = deep_supervision
  
      if self.mask_classification:
          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 math
  import copy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.utils import utils
  from paddleseg.core.train import check_logits_losses
label_desc: Use image node_id 1 to create a new TransformerPredictor object from inherited base classes: nn with example: obj = TransformerPredictor(in_channels, mask_classification, num_classes, hidden_dim, num_queries, nheads, dropout, dim_feedforward, enc_layers, dec_layers, pre_norm, deep_supervision, mask_dim, enforce_input_project)