* or extract a variable, and put the suppression on that. However, a local variable typically
   * doesn't work: Because nullness analyses typically infer the nullness of local variables,
   * there's no way to assign a {@code @Nullable T} to a field {@code T foo;} and instruct the
   * analysis that that means "plain {@code T}" rather than the inferred type {@code @Nullable T}.

   * </ul>
   *
   * <p>This method is suitable for the common use case of dividing work among buckets that meet the
   * following conditions:
   *
   * <ul>
   *   <li>You want to assign the same fraction of inputs to each bucket.
   *   <li>When you reduce the number of buckets, you can accept that the most recently added

TEST(CAPI_EXPERIMENTAL, IsStateful) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  int assign = TF_OpIsStateful("AssignAddVariableOp", status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  EXPECT_EQ(assign, 1);
  int id = TF_OpIsStateful("Identity", status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  EXPECT_EQ(id, 0);

!!! tip
    You don't have to add metadata for all the tags that you use.

### Use your tags

Use the `tags` parameter with your *path operations* (and `APIRouter`s) to assign them to different tags:

```Python hl_lines="21  26"
{!../../../docs_src/metadata/tutorial004.py!}
```

!!! info

        <option value="$PROJECT_DIR$/plugins/sam-with-receiver/testData" />
        <option value="$PROJECT_DIR$/plugins/kotlinx-serialization/testData" />
        <option value="$PROJECT_DIR$/plugins/assign-plugin/testData" />
        <option value="$PROJECT_DIR$/plugins/power-assert/testData" />
        <option value="$PROJECT_DIR$/analysis/analysis-api/testData" />

  ASSERT_TRUE(executed);
  auto handle_cleaner = tensorflow::gtl::MakeCleanup(
      [var_handle]() { TFE_DeleteTensorHandle(var_handle); });

  // Assign to the variable, copying to the custom device.
  std::unique_ptr<TFE_TensorHandle, decltype(&TFE_DeleteTensorHandle)> one(
      TestScalarTensorHandle(context.get(), 111.f), TFE_DeleteTensorHandle);

      i++
    }
    val groupLength = i - groupOffset
    if (groupLength == 0 || groupLength > 4) return null // Group is the wrong size.

    // We've successfully read a group. Assign its value to our byte array.
    address[b++] = (value.ushr(8) and 0xff).toByte()
    address[b++] = (value and 0xff).toByte()
  }

  // All done. If compression happened, we need to move bytes to the right place in the

pkg encoding/base32, method (Encoding) WithPadding(int32) *Encoding
pkg encoding/csv, type Reader struct, ReuseRecord bool
pkg encoding/json, func Valid([]uint8) bool
pkg go/ast, type TypeSpec struct, Assign token.Pos
pkg go/types, func SizesFor(string, string) Sizes
pkg go/types, method (*TypeName) IsAlias() bool
pkg hash/fnv, func New128() hash.Hash
pkg hash/fnv, func New128a() hash.Hash

		}

	}
	return false
}

// ParseLifecycleConfigWithID - parses for a Lifecycle config and assigns
// unique id to rules with empty ID.
func ParseLifecycleConfigWithID(r io.Reader) (*Lifecycle, error) {
	var lc Lifecycle
	if err := xml.NewDecoder(r).Decode(&lc); err != nil {
		return nil, err
	}
	// assign a unique id for rules with empty ID
	for i := range lc.Rules {
		if lc.Rules[i].ID == "" {

   * was acquired, or null if no connection was acquired. The acquired connection will also be
   * given to [connectionUser] who may (for example) assign it to a [RealCall.connection].
   *
   * This confirms the returned connection is healthy before returning it. If this encounters any
   * unhealthy connections in its search, this will clean them up.
   *