│   └── test_main.py
```

Let's say that now the file `main.py` with your **FastAPI** app has some other **path operations**.

It has a `GET` operation that could return an error.

It has a `POST` operation that could return several errors.

Both *path operations* require an `X-Token` header.

//// tab | Python 3.10+

```Python
{!> ../../docs_src/app_testing/app_b_an_py310/main.py!}
```

////

            same[k] += 1;
          } else {
            diff[k] += 1;
          }
        }
      }
      // measure probability and assert it's within margin of error
      for (int j = 0; j < hashBits; j++) {
        double prob = (double) diff[j] / (double) (diff[j] + same[j]);
        assertThat(prob).isWithin(epsilon).of(0.50d);
      }
    }
  }

  /**

     * Processes a suggest request.
     * @param request The HTTP request.
     * @param response The HTTP response.
     * @param chain The filter chain.
     * @throws IOException If an I/O error occurs.
     * @throws ServletException If a servlet error occurs.
     */
    protected void processSuggestRequest(final HttpServletRequest request, final HttpServletResponse response, final FilterChain chain)

We verify that we get a `username`, and extract the scopes.

And then we validate that data with the Pydantic model (catching the `ValidationError` exception), and if we get an error reading the JWT token or validating the data with Pydantic, we raise the `HTTPException` we created before.

For that, we update the Pydantic model `TokenData` with a new property `scopes`.

  loadImage = function(img, url, limit) {
    var imgData = new Image();
    $(imgData).on("load", function() {
      $(img).css("background-image", "");
      $(img).attr("src", url);
    });
    $(imgData).on("error", function() {
      if (limit > 0) {
        setTimeout(function() {
          loadImage(img, url, --limit);
        }, IMG_LOADING_DELAY);
      } else {

// loadBucketReplicationMetrics - `BucketMetricsLoaderFn` for bucket replication metrics
// such as latency and sent bytes.
func loadBucketReplicationMetrics(ctx context.Context, m MetricValues, c *metricsCache, buckets []string) error {
	if globalSiteReplicationSys.isEnabled() {
		return nil
	}

	dataUsageInfo, err := c.dataUsageInfo.Get()
	if err != nil {
		metricsLogIf(ctx, err)
		return nil
	}

## What is Bit Rot protection?

Bit Rot, also known as data rot or silent data corruption is a data loss issue faced by disk drives today. Data on the drive may silently get corrupted without signaling an error has occurred, making bit rot more dangerous than a permanent hard drive failure.

MinIO's erasure coded backend uses high speed [HighwayHash](https://github.com/minio/highwayhash) checksums to protect against Bit Rot.

		m.Set(processFileDescriptorOpenTotal, float64(openFDs))
	}
}

// loadProcessMetrics - `MetricsLoaderFn` for process metrics
func loadProcessMetrics(ctx context.Context, m MetricValues, c *metricsCache) error {
	m.Set(processGoRoutineTotal, float64(runtime.NumGoroutine()))

	if !globalBootTime.IsZero() {
		m.Set(processUptimeSeconds, time.Since(globalBootTime).Seconds())
	}

If you open in your browser a URL like:

```
http://127.0.0.1:8000/items/foo-item
```

...without adding the required parameter `needy`, you will see an error like:

```JSON
{
  "detail": [
    {
      "type": "missing",
      "loc": [
        "query",
        "needy"
      ],
      "msg": "Field required",
      "input": null
    }

That way I could find the best ways to reduce code duplication as much as possible, to have completion everywhere, type and error checks, etc.

All in a way that provided the best development experience for all the developers.

## Requirements { #requirements }