* is by declaring that step to be the last step of the pipeline. Nevertheless, we refer to the
 * "value" of a successful step or the "result" (value or exception) of any step.
 *
 * <ol>
 *   <li>A pipeline starts at its leaf step (or steps), which is created from either a callable
 *       block or a {@link ListenableFuture}.

### Install `tuned.conf` performance profile

#### Step 1 - download `tuned.conf` from the referenced link
```
wget https://raw.githubusercontent.com/minio/minio/master/docs/tuning/tuned.conf
```

#### Step 2 - install tuned.conf as supported performance profile on all nodes
```
sudo mkdir -p /usr/lib/tuned/minio/
sudo mv tuned.conf /usr/lib/tuned/minio
```

#### Step 3 - to enable minio performance profile on all the nodes
```

## Making changes

### Step 1. Make changes in charts and values.yaml in `manifests` directory

Be sure to provide sufficient documentation and example usage in values.yaml.

- If the chart has a `values.schema.json`, that should be updated as well.

### Step 2. Update the istioctl/Operator values

If you are modifying the `gateway` chart, you can stop here.

          "instant": true,
          "interval": "",
          "intervalFactor": 1,
          "legendFormat": "{{instance}}",
          "metric": "process_start_time_seconds",
          "refId": "A",
          "step": 60
        }
      ],
      "title": "Uptime",
      "type": "stat"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"
      },

resulting CR is converted to Helm values.yaml format and passed to the next step.
1. Part of the configuration profile contains settings in the Helm values.yaml schema format. User overrides of
these fields are applied and merged with the output of this step. The result of this step is a merge of configuration

You could also use it to generate code automatically, for clients that communicate with your API. For example, frontend, mobile or IoT applications.

## Recap, step by step

### Step 1: import `FastAPI`

{* ../../docs_src/first_steps/tutorial001.py hl[1] *}

`FastAPI` is a Python class that provides all the functionality for your API.

/// note | "Technical Details"

# Tutorial - User Guide

This tutorial shows you how to use **FastAPI** with most of its features, step by step.

Each section gradually builds on the previous ones, but it's structured to separate topics, so that you can go directly to any specific one to solve your specific API needs.

It is also built to work as a future reference so you can come back and see exactly what you need.

## Run the code

namespace gradients {

using namespace std;

// ================== Helper functions =================

// Fills data with values [start,end) with given step size.
void Range(vector<int32_t>* data, int32_t start, int32_t end,
           int32_t step = 1) {
  for (int32_t i = start; i < end; i += step) {
    (*data)[i] = i;
  }
}

// Fills out_dims with the dimensions of the given tensor.
void GetDims(const TF_Tensor* t, int64_t* out_dims) {

        Doc(
            """
            'Whitelist' validation step. The parameter field will be the single one
            allowed by the alias or set of aliases defined.
            """
        ),
    ] = None,
    serialization_alias: Annotated[
        Union[str, None],
        Doc(
            """
            'Blacklist' validation step. The vanilla parameter field will be the

  virtual void ClearCachesAndThreadExecutors() = 0;

  // Initialize the step resource container for a training step. This is used
  // in current TF runtime. For tfrt, it is used by fallback op handler.
  virtual void StartStep() = 0;
  // Destroy the step resource container for a training step.
  virtual void EndStep() = 0;

  // Return the Eager Executor for current thread. Please note that Eager