tools: a memory profiler to visualize your model’s memory usage over time
    and a [python tracer](https://www.tensorflow.org/guide/profiler#events)
    which allows you to trace python function calls in your model. Usability
    improvements include better diagnostic messages and
    [profile options](https://tensorflow.org/guide/profiler#collect_performance_data)
    to customize the host and device trace verbosity level.

                              Many .condition.type values are consistent across resources like Available, but because arbitrary conditions can be
                              useful (see .node.status.conditions), the ability to deconflict is important.
                              The regex it matches is (dns1123SubdomainFmt/)?(qualifiedNameFmt)
                            maxLength: 316

- Adds the ability to disable Accelerator/GPU metrics collected by Kubelet ([#91930](https://github.com/kubernetes/kubernetes/pull/91930), [@RenaudWasTaken](https://github.com/RenaudWasTaken)) [SIG Node]

  let summary = "Invokes a StableHLO module.";

  let description = [{
This op is used with JAX native serialization in a TensorFlow context with
stability guarantees.
  }];

  let arguments = (ins
    Arg<Variadic<TF_Tensor>, [{A list of `Tensor` with possibly different types to be passed as arguments

32CD          ; mapped                 ; 0065 0072 0067 #4.0  SQUARE ERG
32CE          ; mapped                 ; 0065 0076     # 4.0  SQUARE EV
32CF          ; mapped                 ; 006C 0074 0064 #4.0  LIMITED LIABILITY SIGN
32D0          ; mapped                 ; 30A2          # 1.1  CIRCLED KATAKANA A
32D1          ; mapped                 ; 30A4          # 1.1  CIRCLED KATAKANA I

        "properties": {
          "apiVersion": {