code: def get_dice_score(
      prediction, target, channel_axis=1, smooth_nr=1e-6, smooth_dr=1e-6
  ):
      channel_axis, reduce_axis = 1, tuple(
          range(2, len(prediction.shape))
      )
      prediction = prediction.argmax(axis=channel_axis)
      prediction, target = (
          one_hot(prediction)[:, 1:],
          one_hot(target)[:, 1:],
      )
      intersection = np.sum(prediction * target, axis=reduce_axis)
      target_sum = np.sum(target, axis=reduce_axis)
      prediction_sum = np.sum(prediction, axis=reduce_axis)
      result = (2.0 * intersection + smooth_nr) / (
          target_sum + prediction_sum + smooth_dr
      )
      return result[0]
  
file_path: tinygrad/examples/mlperf/metrics.py
imports: import re
  import string
  from collections import Counter
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: get_dice_score(prediction, target, channel_axis, smooth_nr, smooth_dr) and returns: result