wa := obj.(webhookAnalysis)
		row := table.Row{
			Cells: make([]table.Cell, 0),
		}
		row.Cells = append(row.Cells, table.NewCell(wa.Name), table.NewCell(wa.Revision))
		if wa.Injected {
			row.Cells = append(row.Cells, table.NewCell("✔", color.FgGreen))
		} else {
			row.Cells = append(row.Cells, table.NewCell("✘", color.FgRed))
		}
		row.Cells = append(row.Cells, table.NewCell(wa.Reason))
		return row
	})

	"github.com/cespare/xxhash/v2"
	jsoniter "github.com/json-iterator/go"
)

// XL constants.
const (
	// XL metadata file carries per object metadata.
	xlStorageFormatFileV1 = "xl.json"
)

// Valid - tells us if the format is sane by validating
// format version and erasure coding information.
func (m *xlMetaV1Object) valid() bool {
	return isXLMetaFormatValid(m.Version, m.Format) &&

    The `--no-cache-dir` option tells `pip` to not save the downloaded packages locally, as that is only if `pip` was going to be run again to install the same packages, but that's not the case when working with containers.

    !!! note
        The `--no-cache-dir` is only related to `pip`, it has nothing to do with Docker or containers.

    The `--upgrade` option tells `pip` to upgrade the packages if they are already installed.

    * Here we pass the class that Gunicorn can import and use with:

        ```Python
        import uvicorn.workers.UvicornWorker
        ```

* `--bind`: This tells Gunicorn the IP and the port to listen to, using a colon (`:`) to separate the IP and the port.
    * If you were running Uvicorn directly, instead of `--bind 0.0.0.0:80` (the Gunicorn option) you would use `--host 0.0.0.0` and `--port 80`.

Then create a file `traefik.toml` with:

```TOML hl_lines="3"
[entryPoints]
  [entryPoints.http]
    address = ":9999"

[providers]
  [providers.file]
    filename = "routes.toml"
```

This tells Traefik to listen on port 9999 and to use another file `routes.toml`.

!!! tip
    We are using port 9999 instead of the standard HTTP port 80 so that you don't have to run it with admin (`sudo`) privileges.

```Python
burgers = await get_burgers(2)
```

The key here is the `await`. It tells Python that it has to wait ⏸ for `get_burgers(2)` to finish doing its thing 🕙 before storing the results in `burgers`. With that, Python will know that it can go and do something else 🔀 ⏯ in the meanwhile (like receiving another request).

}

// ShardSize - returns actual shared size from erasure blockSize.
func (e ErasureInfo) ShardSize() int64 {
	return ceilFrac(e.BlockSize, int64(e.DataBlocks))
}

// IsValid - tells if erasure info fields are valid.
func (fi FileInfo) IsValid() bool {
	if fi.Deleted {
		// Delete marker has no data, no need to check
		// for erasure coding information
		return true
	}

```
.
└── sql_app
    ├── __init__.py
    ├── crud.py
    ├── database.py
    ├── main.py
    ├── models.py
    └── schemas.py
```

The file `__init__.py` is just an empty file, but it tells Python that `sql_app` with all its modules (Python files) is a package.

Now let's see what each file/module does.

## Install `SQLAlchemy`

First you need to install `SQLAlchemy`:

<div class="termy">

    *   Add support for passing list of lists to the `metrics` argument in Keras
        `compile`.
    *   Add `tf.keras.layers.AbstractRNNCell` as the preferred implementation
        for RNN cells in TF v2. User can use it to implement RNN cells with
        custom behavior.
    *   Keras training and validation curves are shown on the same plot when
        using the TensorBoard callback.

2. By using a `with` block, we make sure that the file-like object is closed after the generator function is done. So, after it finishes sending the response.
3. This `yield from` tells the function to iterate over that thing named `file_like`. And then, for each part iterated, yield that part as coming from this generator function.