TF_Status* status);

// Enables only graph collection in RunMetadata on the functions executed from
// this context.
TF_CAPI_EXPORT extern void TFE_ContextEnableGraphCollection(TFE_Context* ctx);

// Disables only graph collection in RunMetadata on the functions executed from
// this context.
TF_CAPI_EXPORT extern void TFE_ContextDisableGraphCollection(TFE_Context* ctx);

```Python hl_lines="6"
{!../../docs_src/path_operation_advanced_configuration/tutorial001.py!}
```

### Using the *path operation function* name as the operationId

If you want to use your APIs' function names as `operationId`s, you can iterate over all of them and override each *path operation's* `operation_id` using their `APIRoute.name`.

You should do it after adding all your *path operations*.

// or a function call.
type PrimaryTerm struct {
	Value         *LitValue   `parser:"  @@"`
	JPathExpr     *JSONPath   `parser:"| @@"`
	ListExpr      *ListExpr   `parser:"| @@"`
	SubExpression *Expression `parser:"| \"(\" @@ \")\""`
	// Include function expressions here.
	FuncCall *FuncExpr `parser:"| @@"`
}

// FuncExpr represents a function call
type FuncExpr struct {

*   `tf.function`:

    *   `tf.function` now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:
        *   Using `functools.wraps` on a function with different signature

// GraphDef type. These functions must be deleted by calling TF_DeleteFunction.
//
// If `mutate_proto_func` is non-NULL, run it over each FunctionDef proto,
// before creating a TF_Function out of the possibly mutated proto.
static std::vector<UniqueFuncPtr> CreateFunctionsFromTextProto(
    const char* text_proto,
    std::function<void(FunctionDef*)>* mutate_proto_func, TF_Status* status) {

# Body - Fields

The same way you can declare additional validation and metadata in *path operation function* parameters with `Query`, `Path` and `Body`, you can declare validation and metadata inside of Pydantic models using Pydantic's `Field`.

## Import `Field`

First, you have to import it:

{* ../../docs_src/body_fields/tutorial001_an_py310.py hl[4] *}


/// warning

import com.google.common.annotations.GwtCompatible;
import java.util.Arrays;
import javax.annotation.CheckForNull;
import org.checkerframework.checker.nullness.qual.Nullable;

/**
 * Helper functions that can operate on any {@code Object}.
 *
 * <p>See the Guava User Guide on <a
 * href="https://github.com/google/guava/wiki/CommonObjectUtilitiesExplained">writing {@code Object}
 * methods with {@code Objects}</a>.
 *

	if f.Ref == nil {
		f.Ref = make([]*Ref, 0, 8)
	}
	f.walk(ast2, ctxProg, (*File).validateIdents)
	f.walk(ast2, ctxProg, (*File).saveExprs)

	// Accumulate exported functions.
	// The comments are only on ast1 but we need to
	// save the function bodies from ast2.
	// The first walk fills in ExpFunc, and the
	// second walk changes the entries to
	// refer to ast2 instead.
	f.walk(ast1, ctxProg, (*File).saveExport)

To configure them, pass the `swagger_ui_parameters` argument when creating the `FastAPI()` app object or to the `get_swagger_ui_html()` function.

`swagger_ui_parameters` receives a dictionary with the configurations passed to Swagger UI directly.

FastAPI converts the configurations to **JSON** to make them compatible with JavaScript, as that's what Swagger UI needs.

   * {@code function} to the argument, then evaluating using {@code this}. That is, for any pair of
   * non-null objects {@code x} and {@code y}, {@code equivalence.onResultOf(function).equivalent(a,
   * b)} is true if and only if {@code equivalence.equivalent(function.apply(a), function.apply(b))}
   * is true.
   *
   * <p>For example:
   *
   * <pre>{@code