TF Lite produces wrong graph with a sequence of tensor reshape operators
@pkgoogle,
I was able to reproduce the issue on tensorflow v2.13, v2.12 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/616cd0d8c23665915a8fe123d1b348fa/untitled1364.ipynb). I was able to replicate with the same gist, @zichuan-wei, can you please take a look? Thanks.
### 1. System information

- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 20.04
- TensorFlow installation (pip package or built from source): pip
- TensorFlow library (version, if pip package or github SHA, if built from source): 2.15.0-dev20230917

### 2. Code

Provide code to help us reproduce your issues using one of the following options:

```
x1 = tf.constant([[1., 2.], [3., 4.], [5., 6.]], shape=[3, 2])

class Model(tf.keras.Model):

  def __init__(self):
    super(Model, self).__init__()

  @tf.function(input_signature=[tf.TensorSpec(x1.shape, x1.dtype)])
  def call(self, x):
    a = tf.reshape(x, [3, 2, 1])
    b = tf.unstack(a, axis=1)
    c = tf.concat(b, 0)
    d = tf.reshape(c, [3, 2])
    return d

m = Model()
expected_value = m(x1)
print('keras model output:')
print(expected_value.numpy())


converter = tf.lite.TFLiteConverter.from_keras_model(m)
tflite_model = converter.convert()

def _evaluateTFLiteModel(tflite_model, input_data):
    interpreter = tf.lite.Interpreter(model_content=tflite_model)
    interpreter.allocate_tensors()
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    for i in range(len(input_data)):
        interpreter.set_tensor(input_details[i]['index'], input_data[i])
    interpreter.invoke()
    output_data = [interpreter.get_tensor(output_details[i]['index'])
                   for i in range(len(output_details))]
    return output_data


actual_value = _evaluateTFLiteModel(tflite_model,[x1])
print('tflite model output:')
print(actual_value[0])
tf.lite.experimental.Analyzer.analyze(model_content=tflite_model)
```

### 3. Failure after conversion
The model conversion is successful, but it produces wrong results.
Output:
```
keras model output:
[[1. 3.]
 [5. 2.]
 [4. 6.]]
tflite model output:
[[1. 2.]
 [3. 4.]
 [5. 6.]]
```
TFLite ModelAnalyzer:
```
Your TFLite model has '1' subgraph(s). In the subgraph description below,
T# represents the Tensor numbers. Subgraph#0 main(T#0) -> [T#0]

Tensors of Subgraph#0
  T#0(serving_default_args_0:0) shape:[3, 2], type:FLOAT32

---------------------------------------------------------------
Your TFLite model has '1' signature_def(s).

Signature#0 key: 'serving_default'
- Subgraph: Subgraph#0
- Inputs: 
    'args_0' : T#0
- Outputs: 
    'output_1' : T#0
```
