}

// Wrapper to restClient.CallWithMethod to handle network errors, in case of network error the connection is disconnected
// and a healthcheck routine gets invoked that would reconnect.
func (client *storageRESTClient) callGet(ctx context.Context, rpcMethod string, values url.Values, body io.Reader, length int64) (io.ReadCloser, error) {
	if values == nil {
		values = make(url.Values)
	}
	values.Set(storageRESTDiskID, *client.diskID.Load())

  // before `StartExecute` is called again. Using `StartExecute` with `Join`
  // allows the caller to schedule computation on multiple ParallelDevices
  // without sequencing those operations (first call `StartExecute` on each
  // parallel device, then call `Join` on each; even if some of the `Join`s
  // return a bad status the caller must run all of the `Join`s or any future

  typedef void (*MemoryReleaser)(void* data, size_t len, void* arg);

  // Create a tensor instance from the given data buffer and description.
  // `memory_releaser` will be called on destruction, and it's responsible for
  // cleaning up the underlying buffer.
  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, const int64_t* dims, int num_dims, void* data, size_t len,

  // scheduled on that same threadpool, causing a deadlock in cases where the
  // caller of event_mgr->ThenExecute() blocks on the completion of the callback
  // (as in the case of ConstOp kernel creation on GPU, which involves copying a
  // CPU tensor to GPU).
  // Setting a larger thread pool does not help with the Swift caller, as we use
  // a different TFE context for each thread of execution (for running graph

  // finished, if any.
  //
  // `cancellation_manager` must live until after `Join` finishes and pending
  // `is_async` operations finish. In addition to allowing the caller to cancel
  // the operation, its `StartCancel` method will be called if op execution
  // fails on any device in order to cancel the others.
  void StartExecute(TFE_Context* context, const char* operation_name,

string, <code>&amp;s[i]</code> is invalid.
</p>


<h3 id="Array_types">Array types</h3>

<p>
An array is a numbered sequence of elements of a single
type, called the element type.
The number of elements is called the length of the array and is never negative.
</p>

<pre class="ebnf">
ArrayType   = "[" ArrayLength "]" ElementType .
ArrayLength = Expression .
ElementType = Type .
</pre>

{!> ../../docs_src/path_operation_advanced_configuration/tutorial007_pv1.py!}
```

////

/// info

In Pydantic version 1 the method to get the JSON Schema for a model was called `Item.schema()`, in Pydantic version 2, the method is called `Item.model_json_schema()`.

///

### Async Context Manager

If you check, the function is decorated with an `@asynccontextmanager`.

That converts the function into something called an "**async context manager**".

```Python hl_lines="1  13"
{!../../docs_src/events/tutorial003.py!}
```

```

### About `__name__ == "__main__"`

The main purpose of the `__name__ == "__main__"` is to have some code that is executed when your file is called with:

<div class="termy">

```console
$ python myapp.py
```

</div>

but is not called when another file imports it, like in:

```Python
from myapp import app
```

#### More details

Let's say your file is named `myapp.py`.

## pytest.mark.anyio

If we want to call asynchronous functions in our tests, our test functions have to be asynchronous. AnyIO provides a neat plugin for this, that allows us to specify that some test functions are to be called asynchronously.

## HTTPX

Even if your **FastAPI** application uses normal `def` functions instead of `async def`, it is still an `async` application underneath.