How to get tf.data autotune metrics?
Based on the tensorflow documentation, it seems there are different autotune algorithms. 
https://www.tensorflow.org/api_docs/python/tf/data/experimental/AutotuneAlgorithm

Open the **symbols panel** to see the structure in the options.py file below
https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/data/ops/options.py#L540
 @franklsf95,
**tf.data** builds a performance model of the input pipeline and runs an optimization algorithm to find a good allocation of its CPU budget across all parameters specified as **AUTOTUNE**. While the input pipeline is running, `tf.data` tracks the time spent in each operation, so that these times can be fed into the optimization algorithm.

The [OptimizationOptions](https://www.tensorflow.org/api_docs/python/tf/data/experimental/OptimizationOptions?version=stable) object gives some control over how autotune will behave. Thank you! @tilakrayal I understand the autotune algorithm, but want to see how it works in action. Specifically, for example, the code logs the stopping condition for the autotune algorithm (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/model.cc#L2695), which would help me understand how the algorithm works. My question is where can I find these metrics? They must be logged somewhere.
### Issue type

Performance

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

Yes

### Source

source

### TensorFlow version

latest

### Custom code

No

### OS platform and distribution

_No response_

### 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

_No response_

### Current behavior?

I want to better understand the internals of the `tf.data` autotuning algorithm. I found that a lot of metrics are logged in https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/metrics.h and https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/model.cc, but I cannot find in my program runs where these metrics are logged or how I can get programatic access to them.

For example, I want to see how tensorflow models the output latency of my operators, according to Section 3.3.2 of the tf.data paper (https://arxiv.org/abs/2101.12127). Can I somehow get this information from either the Python API or the logs?

cc @tlongeri if you are still working on Tensorflow metrics


### Standalone code to reproduce the issue

```shell
Any tf.data pipeline. I'm using the code from https://www.tensorflow.org/guide/data_performance#reproducing_the_figures
```


### Relevant log output

_No response_