code: def binary_crossentropy(y_true, y_pred):
      batch_size = y_true.shape.as_list()[0]
      batch_size_inv = 1 / batch_size
      out = y_true * tfe.log(y_pred)
      out += (1 - y_true) * tfe.log(1 - y_pred)
      out = out.negative()
      bce = out.reduce_sum(axis=0) * batch_size_inv
      return bce
  
imports: import tf_encrypted
label_desc: Use image node_id 1 for calling a global function with example usage: binary_crossentropy(y_true, y_pred) and returns: bce