Segmentation fault (core dumped)
I made a stupid mistake while doing this experiment. Running this example locally does not give the Segmentation fault (core dumped)...

In my other project which I can not upload I got "Segmentation fault (core dumped)
" error. By providing the real dataset instead of a dummy random dataset for the converter the error went away... Hi @HajarAva, I have successfully ran that example here: [gist](https://colab.sandbox.google.com/gist/pkgoogle/da76735c7ae506cfa2fb5d546f020011/64296.ipynb), can you check if it looks like your example or if you are maybe missing a step? thanks @pkgoogle for following up on this subject...
I don't remember what I was missing but this code is running on my local env now. 

I have "Segmentation fault (core dumped)" error with another project that I can not upload. By replacing the dummy representative dataset with a real representative dataset I could successfully create the full integer tflite model... Ok then... I don't know if I have any other action I can take here, unless you can show me the example with the dummy representative dataset. If you have no more open item, please feel free to close. Thanks! Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64296">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64296">No</a>

### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

2.13.0 

### Custom code

Yes

### OS platform and distribution

Ubuntu 22.04

### Mobile device

_No response_

### Python version

3.10.12

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

RTX 4090

### GPU model and memory

_No response_

### Current behavior?

A similar issue is explained in this [link](https://github.com/tensorflow/tensorflow/issues/61828). I made this new issue as @pkgoogle demanded.
I ran this TensorFlow [example](https://www.tensorflow.org/lite/performance/post_training_integer_quant) locally but I got a Segmentation fault (core dumped) after running this piece of the code:

![image](https://github.com/tensorflow/tensorflow/assets/152485296/fbba4410-0a9b-46ca-8143-09ee0e24dfb1)





### Standalone code to reproduce the issue

```shell
The problem is with running the Post-training integer quantization code of the following example locally:
 
https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/performance/post_training_integer_quant.ipynb#scrollTo=FiwiWU3gHdkW
```


### Relevant log output

```shell
d inputs were expected, but not specified; continuing anyway.
  warnings.warn(
2024-03-22 17:02:49.457966: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:364] Ignored output_format.
2024-03-22 17:02:49.457981: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:367] Ignored drop_control_dependency.
2024-03-22 17:02:49.458398: I tensorflow/cc/saved_model/reader.cc:45] Reading SavedModel from: /tmp/tmpc6h9p_cu
2024-03-22 17:02:49.470594: I tensorflow/cc/saved_model/reader.cc:91] Reading meta graph with tags { serve }
2024-03-22 17:02:49.470607: I tensorflow/cc/saved_model/reader.cc:132] Reading SavedModel debug info (if present) from: /tmp/tmpc6h9p_cu
2024-03-22 17:02:49.512533: I tensorflow/cc/saved_model/loader.cc:231] Restoring SavedModel bundle.
2024-03-22 17:02:49.615602: I tensorflow/cc/saved_model/loader.cc:215] Running initialization op on SavedModel bundle at path: /tmp/tmpc6h9p_cu
2024-03-22 17:02:49.679176: I tensorflow/cc/saved_model/loader.cc:314] SavedModel load for tags { serve }; Status: success: OK. Took 220778 microseconds.
2024-03-22 17:02:49.777449: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-03-22 17:02:49.968916: W tensorflow/compiler/mlir/lite/flatbuffer_export.cc:2073] TFLite interpreter needs to link Flex delegate in order to run the model since it contains the following Select TFop(s):
Flex ops: FlexEmptyTensorList, FlexTensorListPushBack, FlexTensorListReserve, FlexTensorListSetItem, FlexTensorListStack
Details:
        tf.EmptyTensorList(tensor<0xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<!tf_type.variant>>>) : {device = ""}
        tf.EmptyTensorList(tensor<0xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<f32>>>) : {device = ""}
        tf.EmptyTensorList(tensor<0xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<i32>>>) : {device = ""}
        tf.EmptyTensorList(tensor<1xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<128xf32>>>) : {device = ""}
        tf.EmptyTensorList(tensor<1xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<1xi32>>>) : {device = ""}
        tf.EmptyTensorList(tensor<1xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<2xi32>>>) : {device = ""}
        tf.EmptyTensorList(tensor<1xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<384xf32>>>) : {device = ""}
        tf.EmptyTensorList(tensor<2xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<128x128xf32>>>) : {device = ""}
        tf.EmptyTensorList(tensor<2xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<128x384xf32>>>) : {device = ""}
        tf.EmptyTensorList(tensor<2xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<4x128xf32>>>) : {device = ""}
        tf.EmptyTensorList(tensor<2xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<4x64xf32>>>) : {device = ""}
        tf.EmptyTensorList(tensor<2xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<64x128xf32>>>) : {device = ""}
        tf.EmptyTensorList(tensor<2xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<64x384xf32>>>) : {device = ""}
        tf.TensorListPushBack(tensor<!tf_type.variant<tensor<!tf_type.variant>>>, tensor<!tf_type.variant<tensor<4x128xf32>>>) -> (tensor<!tf_type.variant<tensor<!tf_type.variant>>>) : {device = ""}
        tf.TensorListPushBack(tensor<!tf_type.variant<tensor<128x128xf32>>>, tensor<128x128xf32>) -> (tensor<!tf_type.variant<tensor<128x128xf32>>>) : {device = ""}
        tf.TensorListPushBack(tensor<!tf_type.variant<tensor<128x384xf32>>>, tensor<128x384xf32>) -> (tensor<!tf_type.variant<tensor<128x384xf32>>>) : {device = ""}
        tf.TensorListPushBack(tensor<!tf_type.variant<tensor<128xf32>>>, tensor<128xf32>) -> (tensor<!tf_type.variant<tensor<128xf32>>>) : {device = ""}
        tf.TensorListPushBack(tensor<!tf_type.variant<tensor<1xi32>>>, tensor<1xi32>) -> (tensor<!tf_type.variant<tensor<1xi32>>>) : {device = ""}
        tf.TensorListPushBack(tensor<!tf_type.variant<tensor<2xi32>>>, tensor<2xi32>) -> (tensor<!tf_type.variant<tensor<2xi32>>>) : {device = ""}
        tf.TensorListPushBack(tensor<!tf_type.variant<tensor<384xf32>>>, tensor<384xf32>) -> (tensor<!tf_type.variant<tensor<384xf32>>>) : {device = ""}
        tf.TensorListPushBack(tensor<!tf_type.variant<tensor<4x128xf32>>>, tensor<4x128xf32>) -> (tensor<!tf_type.variant<tensor<4x128xf32>>>) : {device = ""}
        tf.TensorListPushBack(tensor<!tf_type.variant<tensor<4x64xf32>>>, tensor<4x64xf32>) -> (tensor<!tf_type.variant<tensor<4x64xf32>>>) : {device = ""}
        tf.TensorListPushBack(tensor<!tf_type.variant<tensor<64x128xf32>>>, tensor<64x128xf32>) -> (tensor<!tf_type.variant<tensor<64x128xf32>>>) : {device = ""}
        tf.TensorListPushBack(tensor<!tf_type.variant<tensor<64x384xf32>>>, tensor<64x384xf32>) -> (tensor<!tf_type.variant<tensor<64x384xf32>>>) : {device = ""}
        tf.TensorListPushBack(tensor<!tf_type.variant<tensor<f32>>>, tensor<f32>) -> (tensor<!tf_type.variant<tensor<f32>>>) : {device = ""}
        tf.TensorListPushBack(tensor<!tf_type.variant<tensor<i32>>>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<i32>>>) : {device = ""}
        tf.TensorListReserve(tensor<2xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<4x128xf32>>>) : {device = ""}
        tf.TensorListSetItem(tensor<!tf_type.variant<tensor<4x128xf32>>>, tensor<i32>, tensor<4x128xf32>) -> (tensor<!tf_type.variant<tensor<4x128xf32>>>) : {device = "", resize_if_index_out_of_bounds = false}
        tf.TensorListStack(tensor<!tf_type.variant<tensor<4x128xf32>>>, tensor<2xi32>) -> (tensor<?x4x128xf32>) : {device = "", num_elements = -1 : i64}
See instructions: https://www.tensorflow.org/lite/guide/ops_select
2024-03-22 17:02:49.968963: I tensorflow/compiler/mlir/lite/flatbuffer_export.cc:2138] Estimated count of arithmetic ops: 1.906 M  ops, equivalently 0.953 M  MACs
INFO: Created TensorFlow Lite delegate for select TF ops.
2024-03-22 17:02:50.038388: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.038506: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.038558: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.038646: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.038691: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.038725: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22058 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4090, pci bus id: 0000:01:00.0, compute capability: 8.9
INFO: TfLiteFlexDelegate delegate: 15 nodes delegated out of 227 nodes with 2 partitions.

2024-03-22 17:02:50.043702: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.043790: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.043838: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.043906: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.043949: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.043986: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22058 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4090, pci bus id: 0000:01:00.0, compute capability: 8.9
fully_quantize: 0, inference_type: 6, input_inference_type: FLOAT32, output_inference_type: FLOAT32
Segmentation fault (core dumped)
```
