"tf.Yield"() : () -> ()
        }, {
        "tf.Yield"() : () -> ()
       }) {is_stateless = true} : (tensor<i1>) -> ()
       "tf.Yield"(%1) : (tensor<i1>) -> ()
    }, {
    ^bb0(%arg1: tensor<i32>, %arg2: tensor<?xf32>):
      "tf.IfRegion"(%0) ({
        %2 = "tf.B"() : () -> (tensor<f32>)
        "tf.Yield"() : () -> ()
        }, {
        "tf.Yield"() : () -> ()

       // CHECK: "tf.Yield"(%[[CONST]])
       %constant = "tf.Const"() {value = dense<0.0> : tensor<4xf32>} : () -> tensor<4xf32>
       "tf.AssignVariableOp"(%0, %constant) : (tensor<*x!tf_type.resource<tensor<4xf32>>>, tensor<4xf32>) -> ()
       "tf.Yield"() : () -> ()
      }, {
       // CHECK: "tf.Yield"(%[[READ]])
       "tf.Yield"() : () -> ()

        rng.uniform(low=0.0, high=1.0, size=static_input_shape).astype(
            np.float32
        )
    )

    def data_gen() -> repr_dataset.RepresentativeDataset:
      for _ in range(100):
        yield {
            'input_tensor': rng.uniform(
                low=0.0, high=1.0, size=static_input_shape
            ).astype(np.float32)
        }

    dataset_path = self.create_tempfile('tfrecord').full_path

  %graph = tf_executor.graph {
    %island, %ctrl = tf_executor.island {
      %pred = "tf.SomeOp"(%arg2) : (tensor<f32>) -> tensor<i32>
      tf_executor.yield %pred : tensor<i32>
    }
    tf_executor.fetch %island : tensor<i32>
  }
  func.return %graph : tensor<i32>
}

// CHECK-LABEL:   func @simple_independent_chains

For example:

```mlir
"tf.IfRegion"(%0) ( {
  %2 = "tf.A"() : () -> (tensor<f32>)
  "tf.Yield"() : () -> ()
  }, {
  "tf.Yield"() : () -> ()
 }) { is_stateless = true } : (tensor<i1>) -> ()
"tf.IfRegion"(%0) ( {
  %2 = "tf.B"() : () -> (tensor<f32>)
  "tf.Yield"() : () -> ()
  }, {
  "tf.Yield"() : () -> ()
  }) { is_stateless = true } : (tensor<i1>) -> ()
```

Would be transformed to:

  ^bb0(%barg0: tensor<f32>, %barg1: tensor<i32>):
    // CHECK: %[[PRED:.*]] = "tf._SomeOp"(%[[BARG1]])
    // CHECK: "tf.Yield"(%[[PRED]])
    %pred = "tf._SomeOp"(%barg1) : (tensor<i32>) -> tensor<i1>
    "tf.Yield" (%pred) : (tensor<i1>) -> ()
  }, {
  // CHECK: ^bb0(%[[BARG0:.*]]: tensor<f32>, %[[BARG1:.*]]: tensor<i32>):
  ^bb0(%barg0: tensor<f32>, %barg1: tensor<i32>):

module {
func.func @simpleWhile(%arg0: tensor<i32>) -> tensor<i32> {
  %0 = "tfl.while"(%arg0) ({
  ^bb0(%block: tensor<i32>):
    "tfl.yield"(%block) : (tensor<i32>) -> ()
  },{
  ^bb0(%block: tensor<i32>):
    "tfl.yield"(%block) : (tensor<i32>) -> ()
  }) {tac.device = "CPU", fused_activation_function = "RELU6", tac.inference_type = "FLOAT"} : (tensor<i32>) -> tensor<i32>
  func.return %0 : tensor<i32>
}

    }
  }];
}

def TF_YieldOp : TF_Op<"Yield",
      [Terminator,
       Pure,
       NativeOpTrait<"ReturnLike", [], "", "">,
       ParentOneOf<["CaseRegionOp", "IfRegionOp", "WhileRegionOp", "GeneratorDatasetRegionOp"]>,
       DeclareOpInterfaceMethods<RegionBranchTerminatorOpInterface,
           ["getMutableSuccessorOperands"]>,
      ]> {
  let summary = "Yield operation";

  let description = [{

		c := field.Name[0]
		if 'a' <= c && c <= 'z' || c == '_' {
			panic("reflect.StructOf: field \"" + field.Name + "\" is unexported but missing PkgPath")
		}
	}

	resolveReflectType(field.Type.common()) // install in runtime
	f := structField{
		Name:   newName(field.Name, string(field.Tag), field.IsExported(), field.Anonymous),
		Typ:    field.Type.common(),
		Offset: 0,
	}
	return f, field.PkgPath
}

      self.scf_scope += 1

  def insert_symbol(self, name, value, type_):
    self.curr_table['symbols'][name] = (value, type_)
    # TODO(mdan): Use the inferred type rather than tracking it here.
    # The following field is deprecated.
    self.curr_table['types'][name] = type_
    return value

  def exit_scope(self):
    self.symbols.pop()
    self.curr_table = self.symbols[len(self.symbols) - 1]
    if self.scf_scope > 0: