code: def cost_function_dict(wcc_weights={0: 0.1, 1: 3.0}, **kwargs):
      """Summary
  
      Parameters
      ----------
      **kwargs
          Description
  
      Returns
      -------
      TYPE
          Description
      """
  
      lossFunc = WeightedCategoricalCrossEntropy(wcc_weights)
  
      return {
          "dice_coef": dice_coef,
          "dice_coef_loss": dice_coef_loss,
          "multi_dice_coef": multi_dice_coef,
          "multi_dice_loss": multi_dice_loss,
          "loss_wcc": lossFunc.loss_wcc,
          "metric_wcc": lossFunc.metric_wcc,
          "loss_wcc_dist": lossFunc.loss_wcc_dist,
          "loss_dice": lossFunc.loss_dice,
          "metric_dice": lossFunc.metric_dice,
          "metric_dice_dist": lossFunc.metric_dice_dist,
          "metric_acc": lossFunc.metric_acc,
      }
  
imports: import numpy
  import itertools
label_desc: Use image node_id 1 for calling a global function with example usage: cost_function_dict(wcc_weights) and returns: dict