code: def dice_coef(y_true, y_pred, smooth=1.0):
      """Summary
  
      Parameters
      ----------
      y_true : Tensor
          Ground truth values for input to a cost function.
      y_pred : Tensor
          Predicted values for input to a cost function.
      smooth : float, optional
          Description
  
      Returns
      -------
      Tensor
          Soft-dice coefficient ranging from 0 to 1.
      """
      from keras import backend as K
  
      y_true_f = K.flatten(y_true)
      y_pred_f = K.flatten(y_pred)
      intersection = K.sum(y_true_f * y_pred_f)
      return (2.0 * intersection + smooth) / (
          K.sum(y_true_f) + K.sum(y_pred_f) + smooth
      )
  
imports: import numpy
  import itertools
label_desc: Use image node_id 2 for calling a global function with example usage: dice_coef(y_true, y_pred, smooth) and returns: unknown