Feature Request: More descriptive get_weights() method
@AAnirudh07 
This issue seems to be a keras related issue. Please post this issue on [keras-team/keras repo.](https://github.com/keras-team/keras/issues)
To know more see;
https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999.

Thank you! Oh cool, I'll do that. Thanks!
### Issue Type

Feature Request

### Tensorflow Version

tf 2.11.0

### OS Platform and Distribution

Windows 11

### Python version

3.10.9


Hello!
Currently, I can get the weights of each layer of a Keras model by calling TensorFlow's `get_weights()` method. Additionally, this method returns the bias of the layer if the `use_bias` term is true. There is no mention of the order in which weights and biases are returned in TensorFlow or Keras documentation. When the method returns two or more NumPy arrays, how can we determine what these layers represent?

It would be awesome if the `get_weights()` method could also indicate whether a particular NumPy array is a weights array, bias array, etc.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

def get_model():
    mnist_model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(256, activation='relu'),
    tf.keras.layers.Dropout(0.45),
    tf.keras.layers.Dense(256, activation='relu'),
    tf.keras.layers.Dropout(0.45),
    tf.keras.layers.Dense(10)])
    return mnist_model


def get_model_state_dict(model):
    for index, layer in enumerate(model.layers):
        print(model.layers[index].get_weights())

state_dict = (get_model_state_dict(get_model()))
```


