"tf.Yield"(%1) : (tensor<i1>) -> ()
  }, {
    ^bb0(%arg2: tensor<i32>, %arg3: tensor<128x1024xf32>):
      %1 = "tf.XlaSharding"(%arg3) <{_XlaSharding = "\0A\0B\0C", sharding = "\0A\0B\0C"}> {unspecified_dims = []} : (tensor<128x1024xf32>) -> tensor<128x1024xf32>
      %2 = "tf.Square"(%1) : (tensor<128x1024xf32>) -> tensor<128x1024xf32>
      "tf.Yield"(%arg2, %2) : (tensor<i32>, tensor<128x1024xf32>) -> ()

          tf_device.return %c, %b : tensor<f32>, tensor<i32>
        }) {device = "CPU:0"} : () -> (tensor<f32>, tensor<i32>)
        %d = "tf.opD"() : () -> tensor<i1>
        tf_executor.yield %a, %launch#0, %launch#1, %d :
                          tensor<i1>, tensor<f32>, tensor<i32>, tensor<i1>
      }
    ```

    Will be transformed into:

    ```mlir
      %island:5 = tf_executor.island {

    for (auto& use : argument.getUses()) {
      if (llvm::isa<func::ReturnOp, TF::YieldOp>(use.getOwner())) {
        erase_indices.set(use.getOperandNumber());
      }
    }
    func.getArgument(arg_index).dropAllUses();
  }
  if (llvm::isa<func::ReturnOp, TF::YieldOp>(func.front().getTerminator())) {
    terminator->eraseOperands(erase_indices);
  }
}

        append(s)
        append('`')
    }

    val sequence: Sequence<PropertyTrace>
        get() = sequence {
            var trace = this@PropertyTrace
            while (true) {
                yield(trace)
                trace = trace.tail ?: break
            }
        }

    private
    val tail: PropertyTrace?
        get() = when (this) {
            is Bean -> trace

        """
        return await super().close()

    @classmethod
    def __get_validators__(cls: Type["UploadFile"]) -> Iterable[Callable[..., Any]]:
        yield cls.validate

    @classmethod
    def validate(cls: Type["UploadFile"], v: Any) -> Any:
        if not isinstance(v, StarletteUploadFile):
            raise ValueError(f"Expected UploadFile, received: {type(v)}")

  std::unique_ptr<Variable> var;
  ASSERT_NE(absl::OkStatus(),
            internal::LoadSavedVariable(context(), saved_variable, &var));
}

// Assigning and reading values should yield
// consistent results.
TEST_P(SavedVariableLoadingTest, AssignAndReadVariableSuccesful) {
  auto& test_params = GetParam();
  DataType dtype = std::get<0>(test_params);

				for _, s := range testData {
					expectMissing(t, s, 0)(m.Load(s))
				}
			}(i)
		}
		wg.Wait()
	})
}

func testAll[K, V comparable](t *testing.T, m *HashTrieMap[K, V], testData map[K]V, yield func(K, V) bool) {
	for k, v := range testData {
		expectStored(t, k, v)(m.LoadOrStore(k, v))
	}
	visited := make(map[K]int)
	m.All()(func(key K, got V) bool {
		want, ok := testData[key]
		if !ok {

//
// In this example, the structural type restriction of P is ~string|int: A|B
// expands to ~string|~[]byte|int|string, which reduces to ~string|~[]byte|int,
// which when intersected with C (~string|~int) yields ~string|int.
//
// NormalTerms computes these expansions and reductions, producing a
// "normalized" form of the embeddings. A structural restriction is normalized

 *
 * That is, this operation does not represent just the execution of task actions.
 *
 * This operation can fail _and_ yield a result.
 * If the operation gets as far as invoking the task executer
 * (i.e. beforeTask callbacks did not fail), then a result is expected.

      // each region and the use of TensorList returned using YieldOp.
      if (auto while_region = llvm::dyn_cast<WhileRegionOp>(use.getOwner())) {
        DCOMMENT("\tTL WhileRegion");
        for (auto branch : while_region.getRegions())
          add_to_worklist(branch->getArgument(use.getOperandNumber()));
        continue;
      }
      if (auto yield = llvm::dyn_cast<YieldOp>(use.getOwner())) {