Lab3 Part2 Section3.4 AttributeError Problem

[zhougengxian]

When I run the second code block, I encounter the following exception.
AttributeError: 'tuple' object has no attribute 'rank'

environment:
tensorflow=2.12.0
capsa=0.1.5

Starting epoch 1/6
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
[<ipython-input-7-830de383472c>](https://localhost:8080/#) in <cell line: 5>()
      8   # Get a batch of training data and compute the training step
      9   for step, data in enumerate(train_loader):
---> 10     metrics = wrapper.train_step(data)
     11     if step % 100 == 0:
     12         print(step)

2 frames
[/usr/local/lib/python3.10/dist-packages/capsa/bias/histogramvae.py](https://localhost:8080/#) in train_step(self, data, prefix)
    225         with tf.GradientTape() as t:
    226             metric_loss, y_hat,bias = self.loss_fn(x, y)
--> 227             compiled_loss = self.compiled_loss(
    228                 y, y_hat, regularization_losses=self.losses
    229             )

[/usr/local/lib/python3.10/dist-packages/keras/engine/compile_utils.py](https://localhost:8080/#) in __call__(self, y_true, y_pred, sample_weight, regularization_losses)
    261                 continue
    262 
--> 263             y_t, y_p, sw = match_dtype_and_rank(y_t, y_p, sw)
    264             sw = losses_utils.apply_mask(y_p, sw, losses_utils.get_mask(y_p))
    265             loss_value = loss_obj(y_t, y_p, sample_weight=sw)

[/usr/local/lib/python3.10/dist-packages/keras/engine/compile_utils.py](https://localhost:8080/#) in match_dtype_and_rank(y_t, y_p, sw)
    829 def match_dtype_and_rank(y_t, y_p, sw):
    830     """Match dtype and rank of predictions."""
--> 831     if y_t.shape.rank == 1 and y_p.shape.rank == 2:
    832         y_t = tf.expand_dims(y_t, axis=-1)
    833     if sw is not None:

AttributeError: 'tuple' object has no attribute 'rank'

Maybe there're some problems with dataloader or capsa library.
I get around this problem by modifying the histogramvae.py in the capsa library, which can be accessed by clicking the second link in the exception stack.

    #@tf.function
    def train_step(self, data, prefix=None):
    # document omitted

        x, y = data
        y = tf.convert_to_tensor(y, dtype=tf.float32) # Add this code into the line 224
        with tf.GradientTape() as t:
            metric_loss, y_hat,bias = self.loss_fn(x, y)
            compiled_loss = self.compiled_loss(
                y, y_hat, regularization_losses=self.losses
            )
            loss = metric_loss + compiled_loss
        .....

        return keras_metrics