code: def dice_coeff(
      x: torch.Tensor,
      target: torch.Tensor,
      ignore_index: int = -100,
      epsilon=1e-6,
  ):
      # Average of Dice coefficient for all batches, or for a single mask
      # 计算一个batch中所有图片某个类别的dice_coefficient
      d = 0.0
      batch_size = x.shape[0]
      for i in range(batch_size):
          x_i = x[i].reshape(-1)
          t_i = target[i].reshape(-1)
          if ignore_index >= 0:
              # 找出mask中不为ignore_index的区域
              roi_mask = torch.ne(t_i, ignore_index)
              x_i = x_i[roi_mask]
              t_i = t_i[roi_mask]
          inter = torch.dot(x_i, t_i)
          sets_sum = torch.sum(x_i) + torch.sum(t_i)
          if sets_sum == 0:
              sets_sum = 2 * inter
  
          d += (2 * inter + epsilon) / (sets_sum + epsilon)
  
      return d / batch_size
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling a global function with example usage: dice_coeff(x, target, ignore_index, epsilon) and returns: unknown