tf.csv

Issue Title,Description,Created At,Comments
[xla:gpu] Extend collective-permute decomposer to also make decision for,"[xla:gpu] Extend collective-permute decomposer to also make decision for
Send-Recv pipeling and record the decision with frontend attributes.

We first use a simple heuristics to decide on the decomposition of which
CollectivePermute operations will be pipelined. We will only pipeline
CollectivePermute that sends loop input data, and pick the first
pipelineable CollectivePermute for pipelining. Then, if there is another
pipelineable CollectivePermute that forms a cycle with the to-be-pipelined
CollectivePermute, we will pipeline both CollectivePermute. Otherwise, we will
only pipeline one CollectivePermute.

Then, when we decompose CollectivePermute operations, we add a frontend
attribute to the Send/Recv operation to represent the pipelining decision.

Add tests.
",2024-03-11T05:16:45Z,0
Microoptmize the conditions in IsArrayType.,"Microoptmize the conditions in IsArrayType.
",2024-03-11T04:30:26Z,0
Do not call Shape::is_static when unnecessary.,"Do not call Shape::is_static when unnecessary.
",2024-03-11T04:26:26Z,0
Eliminate unnecessary copies for HloSharding.,"Eliminate unnecessary copies for HloSharding.
",2024-03-11T04:25:26Z,0
Add Dynamic Range Quantized op support for `op_stat_pass.cc`.,"Add Dynamic Range Quantized op support for `op_stat_pass.cc`.

- Cleanup header imports as well.
",2024-03-11T03:12:47Z,0
Add check conditions in `quantization_driver_test.cc`.,"Add check conditions in `quantization_driver_test.cc`.

- Adds more rigorous checks for desired states in intermediate testing stages.
- Renames and rewrites `IsEmpty` and `HasQuantParams` for clarity.
",2024-03-11T02:17:30Z,0
2.16.1 libtensorflow binary,"### Issue type

Support

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

Yes

### Source

binary

### TensorFlow version

2.16.1

### Custom code

No

### OS platform and distribution

Linux

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

Yes

### Current behavior?

Hi! 

Tensorflow 2.16.1 has been [released](https://github.com/tensorflow/tensorflow/releases/tag/v2.16.1) recently. However, the latest archive with the `libtensorflow` on the official website [is still 2.15](https://www.tensorflow.org/install/lang_c). Where can I get the latest 2.16.1 `libtensorflow` with GPU support for Linux?

### Standalone code to reproduce the issue

```shell
-
```


### Relevant log output

_No response_",2024-03-10T20:56:00Z,0
Make function loading more concurrent with `TF_ENABLE_EAGER_CLIENT_STREAMING_ENQUEUE` set to `false`,"Make function loading more concurrent with `TF_ENABLE_EAGER_CLIENT_STREAMING_ENQUEUE` set to `false`
",2024-03-10T19:12:58Z,0
Testing a temporary code change.,"Testing a temporary code change.
",2024-03-10T18:13:15Z,0
[XLA:Python] Port py_values to nanobind.,"[XLA:Python] Port py_values to nanobind.
",2024-03-10T15:11:31Z,0
tf.tensor_scatter_nd_add: Aborted (core dumped),"### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

tf 2.15

### Custom code

Yes

### OS platform and distribution

Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Under specific input, `tf.tensor_scatter_nd_add` encounters ""Aborted (core dumped)"".

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

# Generate input data
input_tensor = tf.zeros([15, 15, 15])
indices = tf.constant([[[0, 0, 0], [1, 1, 1]], [[2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5]], [[6, 6, 6], [7, 7, 7]], [[8, 8, 8], [9, 9, 9]], [[10, 10, 10], [11, 11, 11]], [[12, 12, 12], [13, 13, 13]], [[14, 14, 14], [0, 0, 0]], [[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]], [[7, 7, 7], [8, 8, 8]], [[9, 9, 9], [10, 10, 10]], [[11, 11, 11], [12, 12, 12]], [[13, 13, 13], [14, 14, 14]]])
updates = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0])  # Cast updates to float

# Invoke tf.tensor_scatter_nd_add
result = tf.tensor_scatter_nd_add(input_tensor, indices, updates)

# Print the result
print(result)
```


### Relevant log output

```shell
2024-03-10 14:59:51.853766: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1)
Aborted (core dumped)
```
",2024-03-10T15:00:49Z,0
tf.raw_ops.UnicodeEncode: Segmentation fault (core dumped),"### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

tf 2.15

### Custom code

Yes

### OS platform and distribution

Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Under specific input, `tf.raw_ops.UnicodeEncode` encounters ""Segmentation fault (core dumped)"".

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

# Generate input data
input_values = tf.constant([72, 101, 108, 108, 111, 32, 87, 111, 114, 108, 100])  # Unicode codepoints for ""Hello World""
input_splits = tf.constant([[0, 5, 11]])  # Split indices for the input_values with two dimensions
output_encoding = ""UTF-8""

# Invoke tf.raw_ops.unicode_encode
output = tf.raw_ops.UnicodeEncode(input_values=input_values, input_splits=input_splits, output_encoding=output_encoding)

# Print the output
print(output)
```


### Relevant log output

```shell
Segmentation fault (core dumped)
```
",2024-03-10T14:59:08Z,0
tf.raw_ops.TensorScatterSub: Aborted (core dumped),"### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

tf 2.15

### Custom code

Yes

### OS platform and distribution

Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Under specific input, `tf.raw_ops.TensorScatterSub` encounters ""Aborted (core dumped)"".

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

# Generate input data
tensor = tf.constant([1, 2, 3, 4, 5])
indices = tf.constant([[[1], [3]], [[0], [2]]])  # Nested structure for indices
updates = tf.constant([10, 20])

# Invoke tf.raw_ops.TensorScatterSub
result = tf.raw_ops.TensorScatterSub(tensor=tensor, indices=indices, updates=updates)

# Print the result
print(result)
```


### Relevant log output

```shell
2024-03-10 14:55:41.958738: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1)
Aborted (core dumped)
```
",2024-03-10T14:57:36Z,0
tf.raw_ops.SparseConcat: Overflow bug ,"### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

tf 2.15

### Custom code

Yes

### OS platform and distribution

Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Under specific input, `tf.raw_ops.SparseConcat` encounters overflow bug.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

# Generate input data
indices1 = tf.constant([[0, 0], [1, 2]], dtype=tf.int64)
values1 = tf.constant([1, 2], dtype=tf.float32)
shape1 = tf.constant([3, 4], dtype=tf.int64)

indices2 = tf.constant([[0, 1], [2, 3]], dtype=tf.int64)
values2 = tf.constant([3, 4], dtype=tf.float32)
shape2 = tf.constant([-1, 4], dtype=tf.int64)  # Mutated shape with the negative bit set

# Invoke tf.raw_ops.SparseConcat
concatenated_sparse = tf.raw_ops.SparseConcat(
    indices=[indices1, indices2],
    values=[values1, values2],
    shapes=[shape1, shape2],
    concat_dim=0
)

print(concatenated_sparse)
```


### Relevant log output

```shell
tensorflow.python.framework.errors_impl.InternalError: {{function_node __wrapped__SparseConcat_N_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} Encountered overflow from large input shape. [Op:SparseConcat] name:
```
",2024-03-10T14:55:13Z,0
tf.raw_ops.FusedPadConv2D: Aborted (core dumped),"### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

tf 2.15

### Custom code

Yes

### OS platform and distribution

Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Under specific input, `tf.raw_ops.FusedPadConv2D` encounters ""Aborted (core dumped)"".

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

# Generate input data
input_data = tf.random.normal([3, 10, 10])

# Define paddings
paddings = tf.constant([[0, 0], [1, 1], [1, 1]])

# Define filter
filter = tf.random.normal([3, 3, 3, 16])

# Define mode
mode = ""REFLECT""  # Change mode to ""REFLECT"" or ""SYMMETRIC""

# Define strides
strides = [1, 1, 1, 1]

# Define padding
padding = ""VALID""

# Invoke tf.raw_ops.FusedPadConv2D
output = tf.raw_ops.FusedPadConv2D(input=input_data, paddings=paddings, filter=filter, mode=mode, strides=strides, padding=padding)

print(output)
```


### Relevant log output

```shell
2024-03-10 14:49:28.555826: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (3 vs. 3)
Aborted (core dumped)
```
",2024-03-10T14:51:07Z,0
tf.tensor_scatter_nd_update: Aborted (core dumped),"### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

tf 2.15

### Custom code

Yes

### OS platform and distribution

Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Under specific input, `tf.tensor_scatter_nd_update` encounters ""Aborted (core dumped)"".

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

# Generate input data
input_tensor = tf.zeros([2, 2, 2])  # A tensor that contains other tensors, creating a nested structure
indices = tf.constant([[[0, 0, 0], [1, 1, 1]], [[1, 0, 1], [0, 1, 0]]])
updates = tf.constant([1, 2], dtype=tf.float32)  # Cast updates to float

# Invoke tf.tensor_scatter_nd_update
result = tf.tensor_scatter_nd_update(input_tensor, indices, updates)

# Print the result
print(result)
```


### Relevant log output

```shell
2024-03-10 14:36:43.315650: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1)
Aborted (core dumped)
```
",2024-03-10T14:48:19Z,0
failed to compile a tensorflow C++ example. # Error incompatible with your Protocol Buffer headers ,"### Issue type

Bug

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

No

### Source

source

### TensorFlow version

tf 2.15.0

### Custom code

No

### OS platform and distribution

Linux Ubuntu 22.04

### Mobile device

_No response_

### Python version

3.10.12

### Bazel version

6.1.0

### GCC/compiler version

11.4.0

### CUDA/cuDNN version

12.2/8.9.7

### GPU model and memory

GTX 3090/24G

### Current behavior?

I first compiled TensorFlow using Bazel according to the official documentation, these are my operations:
`git clone https://github.com/tensorflow/tensorflow`
`cd tensorflow`
`git checkout r2.15`
`./configure `
and information is:

> 
> You have bazel 6.1.0 installed.
> Please specify the location of python. [Default is /usr/bin/python3]: 
> 
> 
> Found possible Python library paths:
>   /usr/lib/python3/dist-packages
>   /usr/local/lib/python3.10/dist-packages
> Please input the desired Python library path to use.  Default is [/usr/lib/python3/dist-packages]
> 
> Do you wish to build TensorFlow with ROCm support? [y/N]: n
> No ROCm support will be enabled for TensorFlow.
> 
> Do you wish to build TensorFlow with CUDA support? [y/N]: y
> CUDA support will be enabled for TensorFlow.
> 
> Do you wish to build TensorFlow with TensorRT support? [y/N]: n
> No TensorRT support will be enabled for TensorFlow.
> 
> Found CUDA 12.2 in:
>     /usr/local/cuda-12.2/targets/x86_64-linux/lib
>     /usr/local/cuda-12.2/targets/x86_64-linux/include
> Found cuDNN 8 in:
>     /usr/lib/x86_64-linux-gnu
>     /usr/include
> 
> 
> Please specify a list of comma-separated CUDA compute capabilities you want to build with.
> You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus. Each capability can be specified as ""x.y"" or ""compute_xy"" to include both virtual and binary GPU code, or as ""sm_xy"" to only include the binary code.
> Please note that each additional compute capability significantly increases your build time and binary size, and that TensorFlow only supports compute capabilities >= 3.5 [Default is: 8.6]: 
> 
> 
> Do you want to use clang as CUDA compiler? [Y/n]: n
> nvcc will be used as CUDA compiler.
> 
> Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: 
> 
> 
> Please specify optimization flags to use during compilation when bazel option ""--config=opt"" is specified [Default is -Wno-sign-compare]: 
> 
> 
> Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
> Not configuring the WORKSPACE for Android builds.
> 
> Preconfigured Bazel build configs. You can use any of the below by adding ""--config=<>"" to your build command. See .bazelrc for more details.
> 	--config=mkl         	# Build with MKL support.
> 	--config=mkl_aarch64 	# Build with oneDNN and Compute Library for the Arm Architecture (ACL).
> 	--config=monolithic  	# Config for mostly static monolithic build.
> 	--config=numa        	# Build with NUMA support.
> 	--config=dynamic_kernels	# (Experimental) Build kernels into separate shared objects.
> 	--config=v1          	# Build with TensorFlow 1 API instead of TF 2 API.
> Preconfigured Bazel build configs to DISABLE default on features:
> 	--config=nogcp       	# Disable GCP support.
> 	--config=nonccl      	# Disable NVIDIA NCCL support.
> Configuration finished

and I then compile with bazel:
`bazel build --config=cuda tensorflow:tensorflow_cc`
`bazel build tensorflow:install_headers`

There were no issues, I successfully compiled the header files and link libraries I wanted in the `bazel-bin` folder.
But when I try to compile a C++ sample:
```
#include <tensorflow/core/platform/env.h>
#include <tensorflow/core/public/session.h>

#include <iostream>

using namespace std;
using namespace tensorflow;

int main()
{
    Session* session;
    Status status = NewSession(SessionOptions(), &session);
    if (!status.ok()) {
        cout << status.ToString() << ""\n"";
        return 1;
    }
    cout << ""Session successfully created.\n"";
}

```

command is 
`g++ -std=c++14 -o tf_example -I/home/wangchen/tensorflow/bazel-bin/tensorflow/include -L/home/wangchen/tensorflow/bazel-bin/tensorflow/libtensorflow_cc -L/home/wangchen/tensorflow/bazel-bin/tensorflow/libtensorflow_framework -ltensorflow_framework -ltensorflow_cc tf_example.cpp `

I got an error #error This file was generated by an older version of protoc which is incompatible with your Protocol Buffer headers. Please regenerate this file with a newer version of protoc.

My protobuf is compiled from official repo, the versions are:
```
{
    ""23.x"": {
        ""protoc_version"": ""23.4"",
        ""lts"": false,
        ""date"": ""2023-07-05"",
        ""languages"": {
            ""cpp"": ""4.23.4"",
            ""csharp"": ""3.23.4"",
            ""java"": ""3.23.4"",
            ""javascript"": ""3.23.4"",
            ""objectivec"": ""3.23.4"",
            ""php"": ""3.23.4"",
            ""python"": ""4.23.4"",
            ""ruby"": ""3.23.4""
        }
    }
}
```
I suspect there might be some protobuf versions that are incompatible with my TensorFlow. 
What methods should I use to obtain the correct version? 
I would greatly appreciate any proposed solutions.


### Standalone code to reproduce the issue

```shell
#include <tensorflow/core/platform/env.h>
#include <tensorflow/core/public/session.h>

#include <iostream>

using namespace std;
using namespace tensorflow;

int main()
{
    Session* session;
    Status status = NewSession(SessionOptions(), &session);
    if (!status.ok()) {
        cout << status.ToString() << ""\n"";
        return 1;
    }
    cout << ""Session successfully created.\n"";
}

```
```


### Relevant log output

```shell
wangchen@wc:~/tfc++test$ g++ -std=c++14 -o tf_example -I/home/wangchen/tensorflow/bazel-bin/tensorflow/include -L/home/wangchen/tensorflow/bazel-bin/tensorflow/libtensorflow_cc -L/home/wangchen/tensorflow/bazel-bin/tensorflow/libtensorflow_framework -ltensorflow_framework -ltensorflow_cc tf_example.cpp 
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tsl/platform/status.h:39,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/platform/status.h:23,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/platform/errors.h:27,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/platform/env.h:27,
                 from tf_example.cpp:1:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tsl/protobuf/error_codes.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tsl/protobuf/error_codes.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tsl/protobuf/error_codes.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:24,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/device_attributes.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/device_attributes.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/device_attributes.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:36,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/attr_value.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/attr_value.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/attr_value.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/attr_value.pb.h:36,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:36,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor.pb.h:33,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/attr_value.pb.h:36,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:36,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/resource_handle.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/resource_handle.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/resource_handle.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/resource_handle.pb.h:33,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor.pb.h:33,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/attr_value.pb.h:36,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:36,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor_shape.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor_shape.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor_shape.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/resource_handle.pb.h:34,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor.pb.h:33,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/attr_value.pb.h:36,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:36,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/types.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/types.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/types.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:37,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/node_def.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/node_def.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/node_def.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/node_def.pb.h:37,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:37,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/full_type.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/full_type.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/full_type.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:38,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/op_def.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/op_def.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/op_def.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:34,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph_debug_info.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph_debug_info.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph_debug_info.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:36,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/versions.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/versions.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/versions.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:30,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:37,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:30,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/cost_graph.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/cost_graph.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/cost_graph.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:39,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:30,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/step_stats.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/step_stats.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/step_stats.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/step_stats.pb.h:36,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:39,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:30,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/allocation_description.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/allocation_description.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/allocation_description.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/step_stats.pb.h:37,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:39,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:30,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor_description.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor_description.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor_description.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:40,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:30,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/cluster.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/cluster.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/cluster.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:41,
                 from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:30,
                 from tf_example.cpp:2:
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/debug.pb.h:17:2: error: #error This file was generated by an older version of protoc which is
   17 | #error This file was generated by an older version of protoc which is
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/debug.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please
   18 | #error incompatible with your Protocol Buffer headers. Please
      |  ^~~~~
/home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/debug.pb.h:19:2: error: #error regenerate this file with a newer version of protoc.
   19 | #error regenerate this file with a newer version of protoc.
      |  ^~~~~
```
",2024-03-10T04:22:46Z,0
Saved model won't load: Unable to synchronously open object (bad local heap signature),"### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

2.16.1

### Custom code

Yes

### OS platform and distribution

windows 10

### Mobile device

_No response_

### Python version

3.12

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Model saved from Python 3.12 tensorflow 2.16.1
model.save('my_model.keras', overwrite=True)

After this the model does not load

### Standalone code to reproduce the issue

```shell
model=tf.keras.models.load_model('my_model.keras', custom_objects=None, compile=True, safe_mode=True)
```


### Relevant log output

```shell
Traceback (most recent call last):
  File ""D:\Project\main.py"", line 391, in <module>
    model=tf.keras.models.load_model('my_model.keras', custom_objects=None, compile=True, safe_mode=True)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File ""D:\Project\venv\Lib\site-packages\keras\src\saving\saving_api.py"", line 176, in load_model
    return saving_lib.load_model(
           ^^^^^^^^^^^^^^^^^^^^^^
  File ""D:\Project\venv\Lib\site-packages\keras\src\saving\saving_lib.py"", line 192, in load_model
    _raise_loading_failure(error_msgs)
  File ""D:\Project\venv\Lib\site-packages\keras\src\saving\saving_lib.py"", line 273, in _raise_loading_failure
    raise ValueError(msg)
ValueError: A total of 13 objects could not be loaded. Example error message for object <Sequential name=sequential, built=True>:

'Unable to synchronously open object (bad local heap signature)'

List of objects that could not be loaded:
[<Sequential name=sequential, built=True>, <TextVectorization name=text_vectorization, built=True>, <StringLookup name=string_lookup_1, built=False>, <Embedding name=embedding, built=True>, <Conv1D name=conv1d, built=True>, <Dropout name=dropout, built=True>, <Conv1D name=conv1d_1, built=True>, <Dropout name=dropout_1, built=True>, <GlobalMaxPooling1D name=global_max_pooling1d, built=True>, <Dense name=dense, built=True>, <Dropout name=dropout_2, built=True>, <Dense name=dense_1, built=True>, <keras.src.optimizers.adam.Adam object at 0x000001C5026B24E0>]
```
",2024-03-10T04:07:46Z,1
Tensorflow import error,"### Issue type

Build/Install

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

Yes

### Source

source

### TensorFlow version

tf 2.13.0

### Custom code

Yes

### OS platform and distribution

Win 11

### Mobile device

_No response_

### Python version

3.9.7

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I intalled tensorflow, but it gives an error when I try to import it.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf
```


### Relevant log output

```shell
runfile('X:/Nano-Photonics and Quantum Optics Lab!/ML Project/Tkinter learning/Tkinter Git - GitLab/Inverse_Design_Periodic_GUI_CustomModern.py', wdir='X:/Nano-Photonics and Quantum Optics Lab!/ML Project/Tkinter learning/Tkinter Git - GitLab')
Traceback (most recent call last):

  File ""X:\Nano-Photonics and Quantum Optics Lab!\ML Project\Tkinter learning\Tkinter Git - GitLab\Inverse_Design_Periodic_GUI_CustomModern.py"", line 20, in <module>
    import tensorflow as tf  #print(tf.__version__)

  File ""C:\Users\athen\anaconda3\lib\site-packages\tensorflow\__init__.py"", line 469, in <module>
    _keras._load()

  File ""C:\Users\athen\anaconda3\lib\site-packages\tensorflow\python\util\lazy_loader.py"", line 41, in _load
    module = importlib.import_module(self.__name__)

  File ""C:\Users\athen\anaconda3\lib\importlib\__init__.py"", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\__init__.py"", line 20, in <module>
    from keras import distribute

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\distribute\__init__.py"", line 18, in <module>
    from keras.distribute import sidecar_evaluator

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\distribute\sidecar_evaluator.py"", line 22, in <module>
    from keras.optimizers.optimizer_experimental import (

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\optimizers\__init__.py"", line 25, in <module>
    from keras import backend

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\backend\__init__.py"", line 3, in <module>
    from keras.backend import experimental

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\backend\experimental\__init__.py"", line 3, in <module>
    from keras.src.backend import disable_tf_random_generator

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\__init__.py"", line 21, in <module>
    from keras.src import applications

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\applications\__init__.py"", line 18, in <module>
    from keras.src.applications.convnext import ConvNeXtBase

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\applications\convnext.py"", line 28, in <module>
    from keras.src import backend

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\backend.py"", line 35, in <module>
    from keras.src.engine import keras_tensor

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\engine\keras_tensor.py"", line 19, in <module>
    from keras.src.utils import object_identity

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\utils\__init__.py"", line 20, in <module>
    from keras.src.saving.serialization_lib import deserialize_keras_object

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\saving\serialization_lib.py"", line 28, in <module>
    from keras.src.saving.legacy.saved_model.utils import in_tf_saved_model_scope

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\saving\legacy\saved_model\utils.py"", line 30, in <module>
    from keras.src.utils.layer_utils import CallFunctionSpec

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\utils\layer_utils.py"", line 26, in <module>
    from keras.src import initializers

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\initializers\__init__.py"", line 23, in <module>
    from keras.src.initializers import initializers_v1

  File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\initializers\initializers_v1.py"", line 32, in <module>
    keras_export(v1=[""keras.initializers.Zeros"", ""keras.initializers.zeros""])(

  File ""C:\Users\athen\anaconda3\lib\site-packages\tensorflow\python\util\tf_export.py"", line 348, in __call__
    self.set_attr(undecorated_func, api_names_attr, self._names)

  File ""C:\Users\athen\anaconda3\lib\site-packages\tensorflow\python\util\tf_export.py"", line 363, in set_attr
    raise SymbolAlreadyExposedError(

SymbolAlreadyExposedError: Symbol Zeros is already exposed as ().
```
",2024-03-10T01:09:44Z,2
TF 2.16.1 Fails to work with GPUs,"### Issue type

Bug

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

No

### Source

binary

### TensorFlow version

TF 2.16.1

### Custom code

No

### OS platform and distribution

Linux Ubuntu 22.04.4 LTS

### Mobile device

_No response_

### Python version

3.10.12

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

12.4

### GPU model and memory

_No response_

### Current behavior?

I created a python venv in which I installed TF 2.16.1 following your instructions: pip install tensorflow
When I run python, import tf,  and issue tf.config.list_physical_devices('GPU') 
I get an empty list  [ ]

I created another python venv, installed TF 2.16.1, only this time with the instructions: 

 python3 -m pip install tensorflow[and-cuda]
 
When I run that version, import tensorflow as tf, and  issue

 tf.config.list_physical_devices('GPU') 

I also get an empty list. 

BTW, I have no problems running  on my box TF 2.15.1 with GPUs. Julia also works just fine with GPUs and so does PyTorch.
the 
 


### Standalone code to reproduce the issue

```shell
Python 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] on linux
Type ""help"", ""copyright"", ""credits"" or ""license"" for more information.
>>> import tensorflow as tf
2024-03-09 19:15:45.018171: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-09 19:15:50.412646: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
>>> tf.__version__
'2.16.1'

tf.config.list_physical_devices('GPU') 
2024-03-09 19:16:28.923792: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] 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
2024-03-09 19:16:29.078379: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
[]
>>>
```


### Relevant log output

_No response_",2024-03-10T00:17:36Z,6
Replace `RemoteTensorHandle` with `TensorProto` for scalars in an `EnqueueRequest` except for `DT_RESOURCE`,"Replace `RemoteTensorHandle` with `TensorProto` for scalars in an `EnqueueRequest` except for `DT_RESOURCE`
",2024-03-09T20:18:30Z,0
tensorflow 2.16.1 build error: Compiling xla/service/cpu/onednn_matmul.cc failed,"### Issue type

Bug

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

No

### Source

source

### TensorFlow version

2.16.1

### Custom code

No

### OS platform and distribution

Linux Ubuntu 22.04

### Mobile device

_No response_

### Python version

3.11.8

### Bazel version

6.5.0

### GCC/compiler version

11.4.0

### CUDA/cuDNN version

12.4/9.0.0.312

### GPU model and memory

NVIDIA GeForce 940MX

### Current behavior?

INFO: Reading 'startup' options from ~/Documents/dev/git/tensorflow/.bazelrc: --windows_enable_symlinks
INFO: Options provided by the client:
  Inherited 'common' options: --isatty=1 --terminal_columns=211
INFO: Reading rc options for 'build' from ~/Documents/dev/git/tensorflow/.bazelrc:
  Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from ~/Documents/dev/git/tensorflow/.bazelrc:
  'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility
INFO: Reading rc options for 'build' from ~/Documents/dev/git/tensorflow/.tf_configure.bazelrc:
  'build' options: --action_env PYTHON_BIN_PATH=~/Documents/dev/programs/miniconda3/envs/tf/bin/python3 --action_env PYTHON_LIB_PATH=~/Documents/dev/programs/miniconda3/envs/tf/lib/python3.11/site-packages --python_path=~/Documents/dev/programs/miniconda3/envs/tf/bin/python3 --action_env CUDA_TOOLKIT_PATH=/usr/local/cuda-12.3 --action_env TF_CUDA_COMPUTE_CAPABILITIES=5.0 --action_env LD_LIBRARY_PATH=/usr/lib/libreoffice/program:/usr/local/cuda/targets/x86_64-linux/lib:/usr/lib/x86_64-linux-gnu --action_env GCC_HOST_COMPILER_PATH=/usr/bin/x86_64-linux-gnu-gcc-11 --config=cuda
INFO: Found applicable config definition build:short_logs in file ~/Documents/dev/git/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file ~/Documents/dev/git/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:cuda in file ~/Documents/dev/git/tensorflow/.bazelrc: --repo_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain --@local_config_cuda//:enable_cuda
INFO: Found applicable config definition build:mkl in file ~/Documents/dev/git/tensorflow/.bazelrc: --define=build_with_mkl=true --define=enable_mkl=true --define=tensorflow_mkldnn_contraction_kernel=0 --define=build_with_openmp=true -c opt
INFO: Found applicable config definition build:opt in file ~/Documents/dev/git/tensorflow/.tf_configure.bazelrc: --copt=-Wno-sign-compare --host_copt=-Wno-sign-compare
INFO: Found applicable config definition build:linux in file ~/Documents/dev/git/tensorflow/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes
INFO: Found applicable config definition build:dynamic_kernels in file ~/Documents/dev/git/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
INFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (711 packages loaded, 51601 targets configured).
INFO: Found 1 target...
ERROR: ~/.cache/bazel/_bazel_vyepishov/cf67b2b2e967476eb2b1ee98e33ab5bd/external/local_xla/xla/service/cpu/BUILD:1638:11: Compiling xla/service/cpu/onednn_matmul.cc failed: (Exit 1): crosstool_wrapper_driver_is_not_gcc failed: error executing command (from target @local_xla//xla/service/cpu:onednn_matmul) external/local_config_cuda/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc -MD -MF bazel-out/k8-opt/bin/external/local_xla/xla/service/cpu/_objs/onednn_matmul/onednn_matmul.pic.d ... (remaining 229 arguments skipped)
In file included from external/local_xla/xla/shape.h:28,
                 from external/local_xla/xla/service/cpu/onednn_matmul.h:21,
                 from external/local_xla/xla/service/cpu/onednn_matmul.cc:18:
external/local_xla/xla/layout.h:377:18: warning: ‘xla::Layout::DimInfo::dim_level_type’ is too small to hold all values of ‘enum xla::DimLevelType’
  377 |     DimLevelType dim_level_type : 6;
      |                  ^~~~~~~~~~~~~~
external/local_xla/xla/layout.h:389:17: warning: ‘xla::Layout::index_primitive_type_’ is too small to hold all values of ‘enum xla::PrimitiveType’
  389 |   PrimitiveType index_primitive_type_ : 8;
      |                 ^~~~~~~~~~~~~~~~~~~~~
external/local_xla/xla/layout.h:390:17: warning: ‘xla::Layout::pointer_primitive_type_’ is too small to hold all values of ‘enum xla::PrimitiveType’
  390 |   PrimitiveType pointer_primitive_type_ : 8;
      |                 ^~~~~~~~~~~~~~~~~~~~~~~
external/local_xla/xla/service/cpu/onednn_matmul.cc: In function ‘void xla::cpu::__xla_cpu_runtime_OneDnnMatMul(void*, void**)’:
external/local_xla/xla/service/cpu/onednn_matmul.cc:186:68: error: cannot convert ‘std::unique_ptr<tsl::OneDnnThreadPool>::pointer’ {aka ‘tsl::OneDnnThreadPool*’} to ‘dnnl::threadpool_interop::threadpool_iface*’
  186 |   auto onednn_stream = MakeOneDnnStream(cpu_engine, thread_pool.get());
      |                                                     ~~~~~~~~~~~~~~~^~
      |                                                                    |
      |                                                                    std::unique_ptr<tsl::OneDnnThreadPool>::pointer {aka tsl::OneDnnThreadPool*}
external/local_xla/xla/service/cpu/onednn_matmul.cc:148:49: note:   initializing argument 2 of ‘dnnl::stream xla::cpu::{anonymous}::MakeOneDnnStream(const dnnl::engine&, dnnl::threadpool_interop::threadpool_iface*)’
  148 |     dnnl::threadpool_interop::threadpool_iface* thread_pool) {
      |     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~
external/local_xla/xla/service/cpu/onednn_matmul.cc: In function ‘void xla::cpu::__xla_cpu_runtime_OneDnnMatMulReorder(void*, void**)’:
external/local_xla/xla/service/cpu/onednn_matmul.cc:322:68: error: cannot convert ‘std::unique_ptr<tsl::OneDnnThreadPool>::pointer’ {aka ‘tsl::OneDnnThreadPool*’} to ‘dnnl::threadpool_interop::threadpool_iface*’
  322 |   auto onednn_stream = MakeOneDnnStream(cpu_engine, thread_pool.get());
      |                                                     ~~~~~~~~~~~~~~~^~
      |                                                                    |
      |                                                                    std::unique_ptr<tsl::OneDnnThreadPool>::pointer {aka tsl::OneDnnThreadPool*}
external/local_xla/xla/service/cpu/onednn_matmul.cc:148:49: note:   initializing argument 2 of ‘dnnl::stream xla::cpu::{anonymous}::MakeOneDnnStream(const dnnl::engine&, dnnl::threadpool_interop::threadpool_iface*)’
  148 |     dnnl::threadpool_interop::threadpool_iface* thread_pool) {
      |     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~
Target //tensorflow/tools/pip_package:build_pip_package failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 16142.186s, Critical Path: 328.40s
INFO: 25824 processes: 8831 internal, 16993 local.
FAILED: Build did NOT complete successfully

### Standalone code to reproduce the issue

```shell
bazel build --config=mkl --config=opt //tensorflow/tools/pip_package:build_pip_package
```


### Relevant log output

```shell
INFO: Reading 'startup' options from ~/Documents/dev/git/tensorflow/.bazelrc: --windows_enable_symlinks
INFO: Options provided by the client:
  Inherited 'common' options: --isatty=1 --terminal_columns=211
INFO: Reading rc options for 'build' from ~/Documents/dev/git/tensorflow/.bazelrc:
  Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from ~/Documents/dev/git/tensorflow/.bazelrc:
  'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility
INFO: Reading rc options for 'build' from ~/Documents/dev/git/tensorflow/.tf_configure.bazelrc:
  'build' options: --action_env PYTHON_BIN_PATH=~/Documents/dev/programs/miniconda3/envs/tf/bin/python3 --action_env PYTHON_LIB_PATH=~/Documents/dev/programs/miniconda3/envs/tf/lib/python3.11/site-packages --python_path=~/Documents/dev/programs/miniconda3/envs/tf/bin/python3 --action_env CUDA_TOOLKIT_PATH=/usr/local/cuda-12.3 --action_env TF_CUDA_COMPUTE_CAPABILITIES=5.0 --action_env LD_LIBRARY_PATH=/usr/lib/libreoffice/program:/usr/local/cuda/targets/x86_64-linux/lib:/usr/lib/x86_64-linux-gnu --action_env GCC_HOST_COMPILER_PATH=/usr/bin/x86_64-linux-gnu-gcc-11 --config=cuda
INFO: Found applicable config definition build:short_logs in file ~/Documents/dev/git/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file ~/Documents/dev/git/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:cuda in file ~/Documents/dev/git/tensorflow/.bazelrc: --repo_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain --@local_config_cuda//:enable_cuda
INFO: Found applicable config definition build:mkl in file ~/Documents/dev/git/tensorflow/.bazelrc: --define=build_with_mkl=true --define=enable_mkl=true --define=tensorflow_mkldnn_contraction_kernel=0 --define=build_with_openmp=true -c opt
INFO: Found applicable config definition build:opt in file ~/Documents/dev/git/tensorflow/.tf_configure.bazelrc: --copt=-Wno-sign-compare --host_copt=-Wno-sign-compare
INFO: Found applicable config definition build:linux in file ~/Documents/dev/git/tensorflow/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes
INFO: Found applicable config definition build:dynamic_kernels in file ~/Documents/dev/git/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
INFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (711 packages loaded, 51601 targets configured).
INFO: Found 1 target...
ERROR: ~/.cache/bazel/_bazel_vyepishov/cf67b2b2e967476eb2b1ee98e33ab5bd/external/local_xla/xla/service/cpu/BUILD:1638:11: Compiling xla/service/cpu/onednn_matmul.cc failed: (Exit 1): crosstool_wrapper_driver_is_not_gcc failed: error executing command (from target @local_xla//xla/service/cpu:onednn_matmul) external/local_config_cuda/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc -MD -MF bazel-out/k8-opt/bin/external/local_xla/xla/service/cpu/_objs/onednn_matmul/onednn_matmul.pic.d ... (remaining 229 arguments skipped)
In file included from external/local_xla/xla/shape.h:28,
                 from external/local_xla/xla/service/cpu/onednn_matmul.h:21,
                 from external/local_xla/xla/service/cpu/onednn_matmul.cc:18:
external/local_xla/xla/layout.h:377:18: warning: ‘xla::Layout::DimInfo::dim_level_type’ is too small to hold all values of ‘enum xla::DimLevelType’
  377 |     DimLevelType dim_level_type : 6;
      |                  ^~~~~~~~~~~~~~
external/local_xla/xla/layout.h:389:17: warning: ‘xla::Layout::index_primitive_type_’ is too small to hold all values of ‘enum xla::PrimitiveType’
  389 |   PrimitiveType index_primitive_type_ : 8;
      |                 ^~~~~~~~~~~~~~~~~~~~~
external/local_xla/xla/layout.h:390:17: warning: ‘xla::Layout::pointer_primitive_type_’ is too small to hold all values of ‘enum xla::PrimitiveType’
  390 |   PrimitiveType pointer_primitive_type_ : 8;
      |                 ^~~~~~~~~~~~~~~~~~~~~~~
external/local_xla/xla/service/cpu/onednn_matmul.cc: In function ‘void xla::cpu::__xla_cpu_runtime_OneDnnMatMul(void*, void**)’:
external/local_xla/xla/service/cpu/onednn_matmul.cc:186:68: error: cannot convert ‘std::unique_ptr<tsl::OneDnnThreadPool>::pointer’ {aka ‘tsl::OneDnnThreadPool*’} to ‘dnnl::threadpool_interop::threadpool_iface*’
  186 |   auto onednn_stream = MakeOneDnnStream(cpu_engine, thread_pool.get());
      |                                                     ~~~~~~~~~~~~~~~^~
      |                                                                    |
      |                                                                    std::unique_ptr<tsl::OneDnnThreadPool>::pointer {aka tsl::OneDnnThreadPool*}
external/local_xla/xla/service/cpu/onednn_matmul.cc:148:49: note:   initializing argument 2 of ‘dnnl::stream xla::cpu::{anonymous}::MakeOneDnnStream(const dnnl::engine&, dnnl::threadpool_interop::threadpool_iface*)’
  148 |     dnnl::threadpool_interop::threadpool_iface* thread_pool) {
      |     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~
external/local_xla/xla/service/cpu/onednn_matmul.cc: In function ‘void xla::cpu::__xla_cpu_runtime_OneDnnMatMulReorder(void*, void**)’:
external/local_xla/xla/service/cpu/onednn_matmul.cc:322:68: error: cannot convert ‘std::unique_ptr<tsl::OneDnnThreadPool>::pointer’ {aka ‘tsl::OneDnnThreadPool*’} to ‘dnnl::threadpool_interop::threadpool_iface*’
  322 |   auto onednn_stream = MakeOneDnnStream(cpu_engine, thread_pool.get());
      |                                                     ~~~~~~~~~~~~~~~^~
      |                                                                    |
      |                                                                    std::unique_ptr<tsl::OneDnnThreadPool>::pointer {aka tsl::OneDnnThreadPool*}
external/local_xla/xla/service/cpu/onednn_matmul.cc:148:49: note:   initializing argument 2 of ‘dnnl::stream xla::cpu::{anonymous}::MakeOneDnnStream(const dnnl::engine&, dnnl::threadpool_interop::threadpool_iface*)’
  148 |     dnnl::threadpool_interop::threadpool_iface* thread_pool) {
      |     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~
Target //tensorflow/tools/pip_package:build_pip_package failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 16142.186s, Critical Path: 328.40s
INFO: 25824 processes: 8831 internal, 16993 local.
FAILED: Build did NOT complete successfully
```
",2024-03-09T20:04:58Z,0
Fix SegFault in Python InterpreterWrapper,"If `InterpreterWrapper::TensorSparsityParameters` encounters Tensors which do not have a `block_map`, a `nullptr` is dereferenced causing AccViol/SegFault.

Add a check for `nullptr`.

Attempts to fix #62058",2024-03-09T19:57:47Z,0
Force an extra step from pred to u32 before then converting to f32 as that can fail on TGP,"Force an extra step from pred to u32 before then converting to f32 as that can fail on TGP
",2024-03-09T19:43:15Z,0
Build error related to XLA and absl,"### Issue type

Build/Install

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

No

### Source

source

### TensorFlow version

2.16.1

### Custom code

No

### OS platform and distribution

Linux Ubuntu 22.04

### Mobile device

_No response_

### Python version

3.11.7

### Bazel version

6.5.0

### GCC/compiler version

11.4.0

### CUDA/cuDNN version

11.8.0/8.9.7.29

### GPU model and memory

_No response_

### Current behavior?

When building TF from source using the Spack package manager, I see the following build failure:
```
ERROR: /tmp/spackkiy_sjk0/dfa266778fb055fec5b77ad2acb73759/external/local_xla/xla/service/gpu/kernels/BUILD:157:13: Compiling xla/service/gpu/kernels/topk_kernel_bfloat16.cu.cc failed: (Exit 1): crosstool_wrapper_driver_is_not_gcc failed: error executing command (from target @local_xla//xla/service/gpu/kernels:topk_kernel_cuda)
...
external/com_google_absl/absl/strings/internal/str_format/bind.h: In constructor ‘absl::lts_20230802::str_format_internal::FormatSpecTemplate<Args>::FormatSpecTemplate(const absl::lts_20230802::str_format_internal::ExtendedParsedFormat<absl::lts_20230802::FormatConversionCharSet(C)...>&)’:
external/com_google_absl/absl/strings/internal/str_format/bind.h:172:1: error: parse error in template argument list
  172 |     CheckArity<sizeof...(C), sizeof...(Args)>();
      | ^   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~              
external/com_google_absl/absl/strings/internal/str_format/bind.h:172:63: error: expected ‘;’ before ‘)’ token
  172 |     CheckArity<sizeof...(C), sizeof...(Args)>();
      |                                                               ^
external/com_google_absl/absl/strings/internal/str_format/bind.h:173:147: error: template argument 1 is invalid
  173 |     CheckMatches<C...>(absl::make_index_sequence<sizeof...(C)>{});
      |                                                                                                                                                   ^
external/com_google_absl/absl/strings/internal/str_format/bind.h:173:151: error: expected primary-expression before ‘{’ token
  173 |     CheckMatches<C...>(absl::make_index_sequence<sizeof...(C)>{});
      |                                                                                                                                                       ^
external/com_google_absl/absl/strings/internal/str_format/bind.h:173:151: error: expected ‘;’ before ‘{’ token
external/com_google_absl/absl/strings/internal/str_format/bind.h:173:153: error: expected primary-expression before ‘)’ token
  173 |     CheckMatches<C...>(absl::make_index_sequence<sizeof...(C)>{});
      |                                                                                                                                                         ^
Target //tensorflow/tools/pip_package:build_pip_package failed to build
INFO: Elapsed time: 1238.631s, Critical Path: 57.51s
INFO: 17066 processes: 6004 internal, 11062 local.
FAILED: Build did NOT complete successfully
```

### Standalone code to reproduce the issue

See the below build log for steps to reproduce the issue.

### Relevant log output

* [build log](https://github.com/tensorflow/tensorflow/files/14547197/spack-build-out.txt)
* [build env](https://github.com/tensorflow/tensorflow/files/14547196/spack-build-env-mods.txt)
",2024-03-09T17:20:29Z,1
core dumped with tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel,"### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

tf 2.15

### Custom code

Yes

### OS platform and distribution

Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

core dumped error with specific input parameters.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

# Generate input data
input_data = tf.constant([[1.5, 2.5, 3.5], [4.5, 5.5, 6.5]])

# Define min and max values per channel
min_per_channel = tf.constant([1.0, 2.0, 3.0])
max_per_channel = tf.constant([2.0, 3.0, 4.0])

# Invoke tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel with inputs as 0-dimensional tensor and max as a 1x3 tensor
quantized_output = tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel(inputs=tf.constant(0.0), min=min_per_channel, max=max_per_channel, num_bits=8, narrow_range=False)

# Print the quantized output
print(quantized_output)
```


### Relevant log output

```shell
2024-03-09 15:02:07.858055: F tensorflow/core/framework/tensor_shape.cc:356] Check failed: d >= 0 (0 vs. -1)
Aborted (core dumped)
```
",2024-03-09T15:03:18Z,0
core dumped with tf.raw_ops.DrawBoundingBoxes and tf.raw_ops.DrawBoundingBoxesV2,"### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

tf 2.15

### Custom code

Yes

### OS platform and distribution

Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

core dumped error with specific input parameters.

### Standalone code to reproduce the issue

1. The code of `tf.raw_ops.DrawBoundingBoxes`:
```shell
import tensorflow as tf
import numpy as np

# Generate input data
batch_size = 1
image_height = 100
image_width = 100
num_channels = 3
num_boxes = 2

images = np.random.rand(image_height, image_width, num_channels).astype(np.float32)  # Remove the batch dimension
boxes = np.random.rand(batch_size, num_boxes, 4).astype(np.float32)

# Invoke tf.raw_ops.DrawBoundingBoxes with a zero-dimensional tensor for images
drawn_images = tf.raw_ops.DrawBoundingBoxes(images=tf.convert_to_tensor(images),
                                           boxes=tf.convert_to_tensor(boxes))

# Print the result
print(drawn_images)
```

2. The code of `tf.raw_ops.DrawBoundingBoxesV2`:
```
import tensorflow as tf
import numpy as np

# Generate input data
image_height = 100
image_width = 100
num_channels = 3
num_boxes = 2

images = tf.random.uniform((image_height, image_width, num_channels))  # Change the shape to satisfy the requirement of a zero-dimensional tensor
boxes = tf.random.uniform((1, num_boxes, 4))
colors = tf.constant([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]])  # Define colors for each bounding box

# Invoke tf.raw_ops.DrawBoundingBoxesV2
output_images = tf.raw_ops.DrawBoundingBoxesV2(images=images, boxes=boxes, colors=colors)

# Print the output images
print(output_images)
```


### Relevant log output

```shell
2024-03-09 14:55:53.834849: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (3 vs. 3)
Aborted (core dumped)
```
",2024-03-09T14:57:17Z,2
Aborted (core dumped) with tf.raw_ops.AvgPoolGrad,"### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

tf 2.15

### Custom code

Yes

### OS platform and distribution

Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

core dumped error with specific input parameters.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

# Generate input data
input_data = tf.random.normal([1, 28, 28, 3])
grad = tf.random.normal([1, 14, 14, 6])  # Change the number of channels in grad tensor

# Perform average pooling
result = tf.nn.avg_pool2d(input_data, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID', data_format='NHWC')

# Compute gradient
grad_result = tf.raw_ops.AvgPoolGrad(orig_input_shape=tf.shape(input_data), grad=grad, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID', data_format='NHWC')

print(grad_result)
```


### Relevant log output

```shell
free(): corrupted unsorted chunks
Aborted (core dumped)
```
",2024-03-09T14:54:40Z,0
Segmentation fault with tf.raw_ops.AudioSpectrogram,"### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

tf 2.15

### Custom code

Yes

### OS platform and distribution

Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Segmentation fault error with specific input parameters.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

# Generate input data
input_data = tf.random.normal([1, 44100], dtype=tf.float32)

# Invoke tf.raw_ops.AudioSpectrogram with a negative window_size
spectrogram = tf.raw_ops.AudioSpectrogram(input=input_data, window_size=-1024, stride=64, magnitude_squared=False)

# Print the spectrogram
print(spectrogram)
```


### Relevant log output

```shell
Segmentation fault (core dumped)
```
",2024-03-09T14:50:26Z,1
core dumped with tf.quantization.fake_quant_with_min_max_vars_per_channel,"### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

tf 2.15

### Custom code

Yes

### OS platform and distribution

Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

core dumped error with specific input parameters.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

input_data = tf.constant(3.0)

min_per_channel = tf.constant(2.0)
max_per_channel = tf.constant(4.0)

quantized_data = tf.quantization.fake_quant_with_min_max_vars_per_channel(input_data, min_per_channel, max_per_channel)
print(quantized_data)
```


### Relevant log output

```shell
2024-03-09 14:43:28.826225: F tensorflow/core/framework/tensor_shape.cc:356] Check failed: d >= 0 (0 vs. -1)
Aborted (core dumped)
```
",2024-03-09T14:47:46Z,0