Error reported when training model using tf 2.x
Hi @panhu ,

It seems you are passing a Keras3 Tensor to tensorflow functions which won't work and raises the above mentioned error.

A KerasTensor is a symbolic placeholder for a shape and dtype, used when constructing Keras Functional models or Keras Functions. You can only use it as input to a Keras layer or a Keras operation (from the namespaces `keras.layers` and `keras.operations`).

For more details please refer documentation [here](https://github.com/keras-team/keras/blob/master/keras/backend/common/keras_tensor.py). Thanks,My tf is 2.13, but there is still an error when using "tensor.numpy()" `numpy()` method can be called on eager mode (i.e eager tensors) but not on graph mode. What should I do when my model has multiple outputs and a custom loss function. I don't seem to be writing correctly：

        enh_spec = Lambda(self.mk_mask)([real,imag,output_mask])
        
        self.enh_real, self.enh_imag = enh_spec[0],enh_spec[1]
        
        enh_frame = Lambda(self.ifftLayer,arguments = {'mode':'real_imag'})(enh_spec)
        enh_frame = enh_frame * self.win
        enh_time = Lambda(self.overlapAddLayer, name = 'enhanced_time')(enh_frame)
        
        self.model = Model(time_dat,[enh_time,enh_spec])

loss function:
    def lossFunction(y_true,y_pred):
            
            loss = tf.squeeze(self.cost_function(y_pred[0],y_true[0])) 
            mag_loss = tf.log(self.spectrum_loss(y_pred[1],y_true[1]) + 1e-8)
            # calculate mean over batches
            loss = tf.reduce_mean(loss)
            return loss + mag_loss
    def spectrum_loss(self,y_pred,y_true):
        
        enh_real = y_pred[:,0]
        enh_imag = y_pred[:,1]
        enh_mag = tf.sqrt(enh_real**2 + enh_imag**2 + 1e-8)
        
        true_real,true_imag = self.stftLayer(y_true[1], mode='real_imag')
        true_mag = tf.sqrt(true_real**2 + true_imag**2 + 1e-8)
        
        loss_real = tf.reduce_mean((enh_real - true_real)**2,)
        loss_imag = tf.reduce_mean((enh_imag - true_imag)**2,)
        loss_mag = tf.reduce_mean((enh_mag - true_mag)**2,) 
        
        return loss_real + loss_imag + loss_mag

How should I solve this problem。 Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62727">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62727">No</a>

### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

tf 2.13

### Custom code

Yes

### OS platform and distribution

linux 

### Mobile device

_No response_

### Python version

3.8

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Error reported when training model using tf 2.x:

TypeError: You are passing KerasTensor(type_spec=TensorSpec(shape=(), dtype=tf.float32, name=None), name='Placeholder:0', description="created by layer 'tf.cast_2'"), an intermediate Keras symbolic input/output, to a TF API that does not allow registering custom dispatchers, such as tf.cond, tf.function, gradient tapes, or tf.map_fn. Keras Functional model construction only supports TF API calls that do support dispatching, such as tf.math.add or tf.reshape. Other APIs cannot be called directly on symbolic Kerasinputs/outputs. You can work around this limitation by putting the operation in a custom Keras layer call and calling that layer on this symbolic input/output.

### Standalone code to reproduce the issue

```shell
What should I do if I don't want to use “import tensorflow.compat.v1 as tf”
```


### Relevant log output

_No response_