* 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`.

    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.

```
/files/{file_path:path}
```

In this case, the name of the parameter is `file_path`, and the last part, `:path`, tells it that the parameter should match any *path*.

So, you can use it with:

```Python hl_lines="6"
{!../../../docs_src/path_params/tutorial004.py!}
```

!!! tip

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.

             * corresponds to the least significant nonzero byte in lw ^ rw, since lw and rw are
             * little-endian. Long.numberOfTrailingZeros(diff) tells us the least significant
             * nonzero bit, and zeroing out the first three bits of L.nTZ gives us the shift to get
             * that least significant nonzero byte.
             */

```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
	}

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.

```
.
└── 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">

    kHasResult,
    kIdle,
    kShuttingDown,
  };

  tensorflow::mutex execution_mutex_;
  ExecutionState execution_state_ TF_GUARDED_BY(execution_mutex_) =
      ExecutionState::kIdle;
  // Tells the worker thread that there is new work.
  tensorflow::condition_variable start_execute_;
  // The worker thread notifies that work has finished.
  tensorflow::condition_variable finished_execute_;