我想创建我的自定义损失函数。首先，模型的输出形状是（None，7，3）。所以我想将输出拆分为3个列表。但我得到如下错误：

OperatorNotAllowedInGraphError: iterating over `tf.Tensor` is not allowed: AutoGraph did convert this function. This might indicate you are trying to use an unsupported feature.

我认为不支持upper_b_true = [m[0] for m in y_true]。我不知道如何解决这个问题。

class new_loss(tf.keras.losses.Loss):
    def __init__(self, tr1, tr2):
        super(new_loss, self).__init__()
        self.tr1 = tr1
        self.tr2 = tr2
    def call(self, y_true, y_pred):
        #pre-determined value
        tr1 = tf.constant(self.tr1)
        tr2 = tf.constant(self.tr2)
        
        #sep
        upper_b_true = [m[0] for m in y_true]
        y_med_true = [m[1] for m in y_true]
        lower_b_true = [m[2] for m in y_true]
        
        upper_b_pred = [m[0] for m in y_pred]
        y_med_pred = [m[1] for m in y_pred]
        lower_b_pred = [m[2] for m in y_pred]
        
        #MSE part
        err = y_med_true - y_med_pred
        mse_loss = tf.math.reduce_mean(tf.math.square(err))
        
        #Narrow bound
        bound_dif = upper_b_pred - lower_b_pred
        bound_loss = tf.math.reduce_mean(bound_dif)
        
        #Prob metric
        in_upper = y_med_pred <= upper_b_pred
        in_lower = y_med_pred >= lower_b_pred
        prob = tf.logical_and(in_upper,in_lower)
        prob = tf.math.reduce_mean(tf.where(prob,1.0,0.0))
        
        return mse_loss + tf.multiply(tr1, bound_loss) + tf.multiply(tr2, prob)

我试图在执行它的同时对它进行部分注解，但我认为问题出在我提到的列表压缩部分。