What is the effect of TF_GUARDED_BY(mu) for variables like Tensor? 
Hi **@Alok-Ranjan23** ,
- TF_GUARDED_BY(mu) is a thread annotation used for indicating that a particular variable or resource is guarded by a mutex mu. This annotation helps the TensorFlow runtime and its tools to understand and enforce thread safety when accessing the variable or resource.
- The variables cached_filter_data_ and cached_filter_md_ are annotated with TF_GUARDED_BY(mu_). This implies that access to these variables should be synchronized using the mutex mu.

Thank you!
 Thanks for your response.
Please let me know the following.
Is it preserving the memory of the node for the next iteration(#iteration = examples/batch_size) or memory of the same node resued in the same iteration?

Thanks,
Alok. Hi **@Alok-Ranjan23** ,
- Sorry for the delay, The TF_GUARDED_BY(mu_) annotation itself does not dictate whether the memory of the node is preserved for the next iteration or reused within the same iteration. Instead, it ensures thread-safe access to the variable.

Thank you! 

### Source

source

### TensorFlow version

TF2.14

### Custom code

Yes

### OS platform and distribution

Linux 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?

What is the significance of TF_GUARDED_BY(mu) of Tensor in this file [mkl_conv_ops.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/mkl/mkl_conv_ops.cc#L1393)? 
How is it affecting the memory use? 
Is it preserving the memory of the node for the next iteration(#iteration = examples/batch_size) or memory of the same node resued in the same iteration?

### Standalone code to reproduce the issue

```shell
private:
  std::shared_ptr<dnnl::memory> fuse_add_src_;
  std::shared_ptr<dnnl::memory> fuse_add_dst_;
  std::vector<int32> strides_;
  std::vector<int32> dilations_;
  std::vector<Tpadding> padding_list_;
  bool is_filter_const_;
  mutex mu_;
  Padding padding_;
  string data_format_str_;
  TensorFormat data_format_;
  Tensor cached_filter_data_ TF_GUARDED_BY(mu_);
#ifndef ENABLE_ONEDNN_V3
  Tensor cached_filter_md_ TF_GUARDED_BY(mu_);
#else
  FilterMemoryDesc cached_filter_md_ TF_GUARDED_BY(mu_);
#endif  // !ENABLE_ONEDNN_V3
```


### Relevant log output

_No response_