`tf.split` or `tf.transpose` cause errors for quantize-aware training with `quantize_apply`
Hi @Janus-Shiau ,

Thanks for reaching us. I have replicated the reported behaviour with TF2.12 and tf-nightly(2.14.0-dev20230528) and attached gists here [TF2.12v](https://colab.research.google.com/gist/SuryanarayanaY/b81d2bc331f6691d9213c931237531aa/60714_2-12.ipynb#scrollTo=y47-_CEf4o3k) and [tf-nightly](https://colab.research.google.com/gist/SuryanarayanaY/be0c3bb90a66cde51eefbc8940c45107/60714_nightly.ipynb).

We need to dig more on this to confirm the root cause. Thanks!


 Hi @SuryanarayanaY,

Thank you for your prompt reply and assistance. Look forward to any update and appreciate your efforts in addressing my request.
 I am new to opensource contributions, can you @SuryanarayanaY  @Janus-Shiau explain me the issue so that I can work on it. Hi @Bogalakalyani ,

You can refer [contributing.md ](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md)file for more details if you willing to contribute. Thanks! I am facing the same issue. Any progress on it?
<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Bug

### Have you reproduced the bug with TF nightly?

Yes

### Source

source

### Tensorflow Version

tf 2.7 & 2.12

### Custom Code

Yes

### OS Platform and Distribution

Ubuntu 18.04

### 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 Behaviour?

We are trying to implement some network like [ShuffleNetV2](https://arxiv.org/abs/1807.11164) but encounter some error when `quantize_apply` the model.

![image](https://user-images.githubusercontent.com/22385182/233325404-e2e502fe-1151-4f4c-9377-570a01681848.png)

I believe ShuffleNet or related ideas are popular in edge devices, please kindly help us to resolve this proble.

Any advice is welcome.

_I apologize for this should have been posted as an issue on Tensorflow Model Optimization. However, since it seems that this problem is not unique to me, I'm posting it here in the hope of receiving appropriate suggestions or assistance._


### System information

TensorFlow version (installed from source or binary): 2.7.0

TensorFlow Model Optimization version (installed from source or binary): 0.7.0

Python version: 3.8.13

**We also try on latest release of both module, but also not working.**

### Describe the current behavior

When running the provided code, either the `tf.transpose` or `tf.split` will cause error to Tensorflow Model Optimization.

The error message due to `tf.split` before convolution layers:

```
ValueError: Exception encountered when calling layer "bn3" (type BatchNormalization).

Shape must be rank 4 but is rank 5 for '{{node bn3/FusedBatchNormV3}} = FusedBatchNormV3[T=DT_FLOAT, U=DT_FLOAT, data_format="NHWC", epsilon=0.001, exponential_avg_factor=1, is_training=false](Placeholder, bn3/ReadVariableOp, bn3/ReadVariableOp_1, bn3/FusedBatchNormV3/ReadVariableOp, bn3/FusedBatchNormV3/ReadVariableOp_1)' with input shapes: [1,?,128,128,32], [32], [32], [32], [32].
```

The error message due to `tf.transpose`:

```
ValueError: Exception encountered when calling layer "tf.compat.v1.transpose" (type TFOpLambda).

Dimension must be 6 but is 5 for '{{node tf.compat.v1.transpose/transpose}} = Transpose[T=DT_FLOAT, Tperm=DT_INT32](tf.compat.v1.transpose/transpose/a, tf.compat.v1.transpose/transpose/perm)' with input shapes: [1,?,128,128,2,32], [5].
```


### Standalone code to reproduce the issue

Just run the following code you will get the error message due to `tf.split`.

```python
from __future__ import annotations

from typing import Callable, Optional

import tensorflow as tf
import tensorflow_model_optimization as tfmot
from tensorflow.keras import layers


SKIP_LAYER = [
    "resize",
    "Resize",
    "reshape",
    "Reshape",
    "concat",
    "Concat",
    "ExpandDims",
    "Repeats",
    "Shape",
    "strided_slice",
    "Tile",
]


def quantize_model(
    model: tf.keras.Model,
    annotate: Optional[Callable] = None,
    quantize_scope: Optional[dict[str, tf.keras.layers.Layer]] = None,
) -> tf.keras.Model:
    quantize_scope = {} if quantize_scope is None else quantize_scope

    def annotate(layer):
        if any([name in layer.name for name in SKIP_LAYER]):
            return layer
        else:
            return tfmot.quantization.keras.quantize_annotate_layer(layer)

    anno_model = tf.keras.models.clone_model(model, clone_function=annotate)
    with tfmot.quantization.keras.quantize_scope(quantize_scope):
        model = tfmot.quantization.keras.quantize_apply(anno_model)

    return model


def channel_shuffle(tensor: tf.Tensor, groups: int = 2) -> tf.Tensor:
    """Channel shuffle operation."""
    _, height, width, num_channels = tensor.shape.as_list()
    assert num_channels % groups == 0

    tensor = tf.reshape(tensor, [-1, height, width, groups, num_channels // groups])
    tensor = tf.transpose(tensor, [0, 1, 2, 4, 3])
    tensor = tf.identity(tensor, name="channel_shuffle")

    tensor = tf.reshape(tensor, [-1, height, width, num_channels])
    return tensor


def simple_nn(img_input: tf.Tensor) -> tf.Tensor:
    latent = layers.Conv2D(32, 1, padding="same", use_bias=False, name="conv1")(img_input)
    latent = layers.BatchNormalization(name="bn1")(latent)
    latent = layers.ReLU(name="relu1")(latent)

    latent = layers.DepthwiseConv2D(3, 1, padding="same", name="conv2")(img_input)
    latent = layers.BatchNormalization(name="bn2")(latent)

    latent = layers.Conv2D(32, 1, padding="same", use_bias=False, name="conv3")(img_input)
    latent = layers.BatchNormalization(name="bn3")(latent)
    latent = layers.ReLU(name="relu3")(latent)

    return latent


def split_like_nn(img_input: tf.Tensor) -> tf.Tensor:
    latent = layers.Conv2D(64, 1, padding="same", use_bias=False, name="conv0")(img_input)
    latent = layers.BatchNormalization(name="bn0")(latent)
    latent = layers.ReLU(name="relu0")(latent)

    latent_0, latent_1 = tf.split(latent, 2, axis=-1)
    latent_0 = simple_nn(latent_0)
    latent = tf.concat([latent_0, latent_1], axis=-1)

    latent = channel_shuffle(latent)

    return latent


if __name__ == "__main__":
    img_input = tf.keras.Input((128, 128, 1), dtype=tf.float32, name="img")

    outputs = split_like_nn(img_input)

    model = tf.keras.Model(inputs=img_input, outputs=outputs, name="PoseNetV2")
    model.summary()

    model_qat = quantize_model(model)
    model_qat.summary()
```


You can just comment the following three lines of code will get the error message from `tf.transpose`.

```python
 latent_0, latent_1 = tf.split(latent, 2, axis=-1)
 latent_0 = simple_nn(latent_0)
 latent = tf.concat([latent_0, latent_1], axis=-1)
```
