Complex dtype input for keras layer in tf2.16+
Hi **@refraction-ray** ,

Sorry for the delay, I tried to run your code on Colab using TF v2.15, 2.16.1 and faced the same issue. Please find the [gist ](https://colab.sandbox.google.com/gist/Venkat6871/ff152f2a9a9ccea82ee442fccaac4677/65306-2-15-2-16-1-nightly.ipynb)here for reference.

Thank you! Hi @refraction-ray ,

In Keras3 complex dtypes not yet included in supported dtypes. Not sure of the reason for same or is it just missed adding.

You can found the allowed input dtypes of Keras3 from [here](https://github.com/keras-team/keras/blob/master/keras/src/backend/common/dtypes.py#L26).

If you have bandwidth you can add complex dtypes to allowed dtypes locally and test with different backends like torch and jax and update us. Thanks! hi @refraction-ray , I am having the exact same problem. Could you find a temporary solution for this?  @myigitavci , my current solution is to fix the tf version <= 2.15
### Issue type

Bug

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

No

### Source

binary

### TensorFlow version

tf2.16.1

### Custom code

Yes

### OS platform and distribution

colab

### Mobile device

_No response_

### Python version

colab default

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

The following code works well for tf2.15 but fails tf2.16.1 with the introduction of keras3,

```
import tensorflow as tf
print(tf.__version__)

class MyDenseLayer(tf.keras.layers.Layer):
    def __init__(self, num_outputs):
        super(MyDenseLayer, self).__init__()
        self.num_outputs = num_outputs

    def build(self, input_shape):
        self.kernel = self.add_weight(shape=[int(input_shape[-1]),
                                         self.num_outputs], )

    def call(self, inputs):
        kernel = tf.cast(self.kernel, tf.complex64)
        return tf.matmul(inputs, kernel)

layer = MyDenseLayer(10)
layer(tf.zeros([10, 5], dtype=tf.complex64))
```


For short, tf2.16.1 with newly introduced keras 3 seems not supporting the input tensor for a layer in complex dtype

### Standalone code to reproduce the issue

```shell
import tensorflow as tf
print(tf.__version__)

class MyDenseLayer(tf.keras.layers.Layer):
    def __init__(self, num_outputs):
        super(MyDenseLayer, self).__init__()
        self.num_outputs = num_outputs

    def build(self, input_shape):
        self.kernel = self.add_weight(shape=[int(input_shape[-1]),
                                         self.num_outputs], )

    def call(self, inputs):
        kernel = tf.cast(self.kernel, tf.complex64)
        return tf.matmul(inputs, kernel)

layer = MyDenseLayer(10)
layer(tf.zeros([10, 5], dtype=tf.complex64))
```


### Relevant log output

```shell
[usr/local/lib/python3.10/dist-packages/keras/src/backend/common/variables.py](https://localhost:8080/#) in standardize_dtype(dtype)
    428 
    429     if dtype not in dtypes.ALLOWED_DTYPES:
--> 430         raise ValueError(f"Invalid dtype: {dtype}")
    431     return dtype
    432 

ValueError: Invalid dtype: complex64
```
