我有一个回归tf.keras.Model，它包含：

• x: tuple[np.ndarray, np.ndarray]，其中两个项目具有不同的形体
• 形状为(128, 1152)和(1, 256)
• y: float

我把我的模型和训练整理成这样：

class MyModel(tf.keras.Model):

    def __init__(self):
        ...  # Omitted for brevity

    def call(self, inputs: tuple[tf.Tensor, tf.Tensor], training=None, mask=None):
        # Unpacks the two-tuple
        weights_1, weights_2 = inputs
        ...  # Omitted for brevity

# NOTE: item 0's shape is (128, 1152), item 1's shape is (1, 256)
datapoint_x: tuple[np.ndarray, np.ndarray]
datapoint_y: float

model = MyModel()
model(inputs=datapoint_x)  # Works fine

然而，当我转到fit模型时，我得到一个Exception：

>>> model.fit(x=datapoint_x, y=np.array(datapoint_y))

Traceback (most recent call last):
  File "/path/to/python3.10/site-packages/IPython/core/interactiveshell.py", line 3433, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-5-a5dfb3dd4846>", line 1, in <module>
    model.fit(x=datapoint_x, y=np.array(datapoint_y))
  File "/path/to/python3.10/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/path/to/python3.10/site-packages/tensorflow/python/framework/tensor_shape.py", line 910, in __getitem__
    return self._dims[key]
IndexError: tuple index out of range

我研究了这个，self._dims是()，key是0。
在一个有两个元组x的数据集上，调用Model.fit的正确方法是什么？