code: def __init__(
      self,
      num_classes,
      weight_ce,
      weight_mask,
      weight_dice,
      eos_coef,
      num_points,
      oversample_ratio,
      importance_sample_ratio,
  ):
      super().__init__()
      self.num_classes = num_classes
      self.weight_ce = weight_ce
      self.weight_mask = weight_mask
      self.weight_dice = weight_dice
  
      # Use a Hungarian matcher
      self.matcher = HungarianMatcher(
          cost_class=weight_ce,
          cost_mask=weight_mask,
          cost_dice=weight_dice,
          num_points=num_points,
      )
      self.eos_coef = eos_coef
      self.empty_weight = paddle.ones((self.num_classes + 1,))
      self.empty_weight[-1] = self.eos_coef
  
      # Pointwise mask loss parameters
      self.num_points = num_points
      self.oversample_ratio = oversample_ratio
      self.importance_sample_ratio = importance_sample_ratio
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from scipy.optimize import linear_sum_assignment
  from paddlepanseg.cvlibs import manager, build_info_dict
  from paddlepanseg.core.runner import PanSegRunner
label_desc: Use image node_id 1 to create a new MaskFormerRunner object from inherited base classes: PanSegRunner with example: obj = MaskFormerRunner(num_classes, weight_ce, weight_mask, weight_dice, eos_coef, num_points, oversample_ratio, importance_sample_ratio)