`GRU` object has no attribute `get_quantizers` Error when using model_save_quantized_weights

## Objective
I'm using QKeras to apply Quantization-Aware Training to my TensorFlow model in order to deploy it on an embedded device. I use the `model_quantize` function to quantize my pre-defined model.

## Issue Encountered:
After training, I attempted to extract the quantized weights to verify that all layers were properly quantized. However, I encountered an error when calling the function `model_save_quantized_weights`: **`GRU` object has no attribute `get_quantizers`**.

I see that QKeras supported `QGRU`, but why it raise that errors? I have thought that because I wrap in the `Bidirectional` layer but as in **QRNNTutorial**, they also can wrap `LSTM` in a `Bidirectional` and do the quantization with the same config.

## Code Snippets:
#### Model Definition:
```python
def create_model(signal_shape):
input_signal = tf.keras.Input(shape=signal_shape, dtype=tf.float64)
ecg_offset = tf.keras.Input(shape=signal_shape, dtype=tf.float64)

inputs = {
'signal': input_signal,
'ecg_offset': ecg_offset
}

signal = inputs['signal']
signal = tf.transpose(signal, perm=(0,2,1))

conv1 = tf.keras.layers.Conv1D(16,3,activation='relu', padding='same')(signal)
conv2 = tf.keras.layers.Conv1D(32,3,activation='relu', padding='same')(tf.keras.layers.MaxPooling1D(pool_size=2)(conv1))
bi_gru = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64, return_sequences=True), merge_mode='sum')(tf.keras.layers.MaxPooling1D(pool_size=2)(conv2))

conv3 = tf.keras.layers.Conv1D(32,3,activation='relu', padding='same')(bi_gru)
dense = tf.keras.layers.Dense(10)(conv3)

outputs = {
'prob:': tf.transpose(tf.keras.layers.Activation("softmax", name="softmax")(dense), perm=(0,2,1))
}

model = tf.keras.Model(inputs=inputs, outputs=outputs)
return model
```
#### Quantizer Configuration (as suggested by the **QRNNTutorial**):
```python
bits = 4
quantizer_config = {
"bidirectional": {
'activation' : f"quantized_tanh({bits})",
'recurrent_activation' : f"quantized_relu(4,0,1)",
'kernel_quantizer' : f"quantized_bits({bits}, alpha='auto')",
'recurrent_quantizer' : f"quantized_bits({bits}, alpha='auto')",
'bias_quantizer' : f"quantized_bits({bits}, alpha='auto')",
},
"dense": {
'kernel_quantizer' : f"quantized_bits({bits}), alpha='auto'",
'bias_quantizer' : f"quantized_bits({bits}), alpha='auto'"
},
"conv1d": {
'kernel_quantizer' : f"quantized_bits({bits}), alpha='auto'",
'bias_quantizer' : f"quantized_bits({bits}), alpha='auto'"
},
```
## Bonus Question:

- Since I'm using `tf.transpose` (which has no trainable weights), do I need to explicitly declare it in `custom_objects` when calling `model_quantize`? I see that `tf.transpose` appears as a Lambda layer in the model summary, but I haven't added it to `custom_objects` so far.
- When I use `activation='relu'` directly in Conv1D, does `model_quantize` automatically replace it with _quantized_relu_ ? Or do I need to refactor the code to apply the activation in a separate layer, or explicitly include in QConv1D in the config dictionary?
- If I write a custom Quantization layer for my tf custom layer (`QCustomLayer` for `CustomLayer` for example), how can I define and use it with Qkeras? Is it done using `_add_supported_quantized_objects(custom_objects)`?

Thank you so much

Start at model_save_quantized_weights and model_quantize, then compare the Bidirectional GRU configuration with the QGRNNTutorial and QGRU references. Reproduce the get_quantizers error using the provided model and configuration, and verify how tf.transpose, Conv1D activations, and custom quantization layers are handled. Done means the quantized weights can be extracted successfully and the custom-object behavior is clarified.

Written by the indexing model from the issue text.