tf.linalg.normalize generates wrong output in tflite version running on mobile GPU
Hi @MinaBabahaji-ML, I followed your directions exactly with a fresh git clone, on this step:

```
adb shell "cd /data/local/tmp && LD_LIBRARY_PATH=. ./model_test --model=model_files/sample.tflite --output_shape=1,26,3"
```

I get this error:
```
terminating with uncaught exception of type std::runtime_error: You must provide input and output shapes.
Aborted 
```

I am using an emulator but I do not believe this issue is related to that, perhaps you are more familiar with your code so if you know how to fix this please let me know. Thank you [pkgoogle](https://github.com/pkgoogle). Could you please confirm that after cloning the repository, you checked out the right branch? (git checkout normalize_layer).

It seems that you are on main branch Hi @MinaBabahaji-ML, Thank you... I have openCL installed on my mac and my AVD has graphics set to Automatic and I run into this issue:
```sh
adb shell "cd /data/local/tmp && LD_LIBRARY_PATH=. ./model_test --model=model_files/sample.tflite --output_shape=1,26,3"
INFO: Created TensorFlow Lite delegate for GPU.
INFO: Initialized TensorFlow Lite runtime.
VERBOSE: Replacing 4 out of 4 node(s) with delegate (TfLiteGpuDelegateV2) node, yielding 1 partitions for the whole graph.
ERROR: Can not open OpenCL library on this device - undefined symbol: clGetCommandBufferInfoKHR
ERROR: Falling back to OpenGL
ERROR: TfLiteGpuDelegate Init: [GL_INVALID_VALUE]: A numeric argument is out of range.
INFO: Created 0 GPU delegate kernels.
ERROR: TfLiteGpuDelegate Prepare: delegate is not initialized
ERROR: Node number 4 (TfLiteGpuDelegateV2) failed to prepare.
ERROR: Restored original execution plan after delegate application failure.
Segmentation fault 
```

This is likely an emulator problem, Hi @miaout17, can you please take a look? Thanks. Hi @MinaBabahaji-ML, we have a new workflow with [AI-Edge-Torch](https://github.com/google-ai-edge/ai-edge-torch), you can find more information here: [googleblog](https://developers.googleblog.com/en/ai-edge-torch-high-performance-inference-of-pytorch-models-on-mobile-devices/). Perhaps this issue does not exhibit itself if you use this workflow.

I have actually created a simple script for converting your model here:

```py
import torch
import torch.nn as nn
import torch.nn.functional as F
import ai_edge_torch


class DummyModel(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        return F.normalize(x, dim=2)


batch_size = 1
input_shape = (batch_size, 26, 3)
model = DummyModel()
sample_inputs = (torch.randn(*input_shape),)

edge_model = ai_edge_torch.convert(model.eval(), sample_inputs)
edge_model.export("dummy_model.tflite")
```

If you want to, you can actually try visualizing the result in [model-explorer](https://github.com/google-ai-edge/model-explorer) as well.

Please try them out and let us know if this resolves your issue. If you still need further help, feel free to open a new issue at the respective repo.
### 1. System information

- Android S10 snapdragon 855
- TensorFlow built from source
- TensorFlow 2.15.0

### 2. Issue Summary:
When running a TensorFlow Lite model (`tf.linalg.normalize `layer) on an Android 12 device with TensorFlow version 2.15 built from source, using the OpenCL delegate, I am experiencing wrong outputs (This is not hapenning for alll the inputs, but some inputs). Model is converted with `fp16` quantization. This issue can't be reproduced with the XNNPACK delegate. The model is shared in the github repository, but I could regenerate the issue with the following dummy model:
```
import tensorflow as tf

def build_dummy_model(input_shape, name="dummy_model"):
    inputs = tf.keras.Input(shape=input_shape, name="input_layer")
    x_norm = tf.linalg.normalize(inputs, axis=2, name=f"{name}_norm_pred_bones")[0]
    model = tf.keras.Model(inputs=inputs, outputs=x_norm, name=name)
    return model

input_shape = (26, 3) 
model = build_dummy_model(input_shape)
```
### 2. Code

I did not encounter any issues in converting the model, but when running the converted model on the phone with the OpenCL delegate, the output has a big distance from XNNPACK delegate and keras model. The code, model and steps to reproduce the problem can be found in the following GitHub repository:
[GitHub Repository Link](https://github.com/MinaBabahaji-ML/GPU_problem/tree/normalize_layer)

### 3. logs:

```
INFO: Created TensorFlow Lite delegate for GPU.
INFO: Initialized TensorFlow Lite runtime.
VERBOSE: Replacing 4 out of 4 node(s) with delegate (TfLiteGpuDelegateV2) node, yielding 1 partitions for the whole graph.
INFO: Initialized OpenCL-based API.
INFO: Created 1 GPU delegate kernels.
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
VERBOSE: Replacing 2 out of 4 node(s) with delegate (TfLiteXNNPackDelegate) node, yielding 3 partitions for the whole graph.
OpenCL output:
-0, 0, 0, -0, 0, 0, 0, 0, -0, -0.0643921, -0.828613, -0.555664, 0.235107, -0.74707, -0.62207, -0.233398, -0.95752, -0.165894, 0.585449, -0.73291, -0.346191, 0.0108871, -0.659668, -0.751465, 0.243408, -0.935059, -0.25708, -0, 0, -0, -0, 0, -0, -0.0350647, 0.999023, -0.00702286, -0, -0, -0, 0, 0, 0, -0.361328, 0.753418, 0.548828, 0.329346, 0.64209, -0.691895, 0.298828, 0.947754, -0.106201, -0.62207, -0.208618, -0.754395, 0.107788, 0.822266, -0.559082, 0.242432, 0.935059, 0.258545, 0.186646, 0.345703, -0.919922, -0.0539856, 0.639648, 0.76709, -0.193604, 0.981445, -0.0037384, -0.0666504, 0.289795, 0.955078, 0.345215, 0.792969, 0.502441, 0.0843506, -0.280762, 0.956055, 
xnnpack output:
-0.104339, 0.939571, 0.326067, -0.953553, 0.295249, 0.0597043, 0.886078, 0.461906, -0.0388445, -0.0644245, -0.828944, -0.555608, 0.235118, -0.747084, -0.62176, -0.23349, -0.958077, -0.166043, 0.585523, -0.733017, -0.346193, 0.0108894, -0.659484, -0.751639, 0.243398, -0.935215, -0.257159, -0.113086, 0.989401, -0.0910858, -0.119016, 0.962996, -0.241812, -0.0350888, 0.999359, -0.00702636, -0.999308, -0.0174536, -0.0328572, 0.999252, 0.0147311, 0.0357636, -0.361413, 0.753619, 0.549035, 0.329349, 0.642243, -0.692137, 0.298923, 0.948343, -0.106257, -0.622155, -0.208634, -0.754582, 0.107761, 0.822209, -0.558891, 0.242461, 0.935058, 0.258611, 0.186559, 0.345574, -0.91966, -0.0539725, 0.639653, 0.766767, -0.193588, 0.981076, -0.00373641, -0.0666187, 0.289595, 0.954828, 0.345324, 0.7928, 0.502215, 0.0843756, -0.28086, 0.956033, 
```