private final ImmutableMap<E, Integer> inputSet;
    private final int mask;

    SubSet(ImmutableMap<E, Integer> inputSet, int mask) {
      this.inputSet = inputSet;
      this.mask = mask;
    }

    @Override
    public Iterator<E> iterator() {
      return new UnmodifiableIterator<E>() {
        final ImmutableList<E> elements = inputSet.keySet().asList();
        int remainingSetBits = mask;

FIELD com.google.common.collect.ImmutableRangeSet.ranges
FIELD com.google.common.collect.ImmutableSet.asList
FIELD com.google.common.collect.Maps.FilteredMapValues.unfiltered
FIELD com.google.common.collect.Sets.SubSet.inputSet
FIELD com.google.common.collect.SingletonImmutableBiMap.inverse
FIELD com.google.common.collect.TreeTraverser.PostOrderNode.childIterator
FIELD com.google.common.collect.TreeTraverser.PreOrderIterator.stack

  TFE_TensorHandle* input_eager = TestScalarTensorHandle(eager_ctx, 2.0f);
  TF_AbstractTensor* input_t =
      TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  TF_ExecuteOperation(fn_op, 1, &input_t, add_outputs, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

tensor<*x!tf_type.resource>, %arg4: tensor<*x!tf_type.resource>) -> (tensor<?x?xf32>, tensor<*xf32>, tensor<?x16384xf32>, tensor<16x112x112x?xf32>, tensor<16x224x224x3xf32>, tensor<*xf32>) attributes {tf.entry_function = {control_outputs = "", inputs = "inputs_0,conv1_conv2d_readvariableop_resource,conv1_biasadd_readvariableop_resource,fc1000_matmul_readvariableop_resource,fc1000_biasadd_readvariableop_resource", outputs = "identity_RetVal,fc1000_matmul_readvariableop_RetVal,flatten_reshape_RetVal,relu_...

  TF_Session* mutable_session() { return session_; }

 private:
  void DeleteInputValues();
  void ResetOutputValues();

  TF_Session* session_;
  std::vector<TF_Output> inputs_;
  std::vector<TF_Tensor*> input_values_;
  std::vector<TF_Output> outputs_;
  std::vector<TF_Tensor*> output_values_;
  std::vector<TF_Operation*> targets_;
};

	}
}

// TestSignV4TrimAll - tests the logic of TrimAll() function
func TestSignV4TrimAll(t *testing.T) {
	testCases := []struct {
		// Input.
		inputStr string
		// Expected result.
		result string
	}{
		{"本語", "本語"},
		{" abc ", "abc"},
		{" a b ", "a b"},
		{"a b ", "a b"},
		{"a  b", "a b"},
		{"a   b", "a b"},
		{"   a   b  c   ", "a b c"},

    ArrayRef<int64_t> shape = arg_shapes[i];
    Type element_type;
    if (auto input_ty =
            mlir::dyn_cast<RankedTensorType>(func_type.getInput(i))) {
      if (input_ty.getRank() != shape.size()) {
        return failure();
      }
      element_type = input_ty.getElementType();
    } else {
      auto unranked_input_ty =

    handle.set_maybe_type_name(type_index.name());

    Tensor* input = new Tensor(host_allocator_, DT_RESOURCE, TensorShape({}));
    input->scalar<ResourceHandle>()() = handle;
    tensors_.push_back(input);
    inputs_.push_back({nullptr, input});
  }

 protected:
  core::RefCountPtr<DeviceContext> device_context_;
  Allocator* host_allocator_;
  Allocator* device_allocator_;

  XlaCompiler::Options compiler_options_;

            dtype: DT_STRING
            tensor_shape {
            }
            string_val: "model"
          }
        }
      }
    }
    node {
      name: "save/StringJoin/inputs_1"
      op: "Const"
      attr {
        key: "_output_shapes"
        value {
          list {
            shape {
            }
          }
        }
      }
      attr {

func.func private @tensor_list_type_array_like() -> !tfr.tensor_list<[N, T]>

// -----

// CHECK-LABEL: tensor_list_type_tuple_like
func.func private @tensor_list_type_tuple_like() -> !tfr.tensor_list<input_T>

// -----

// expected-error@+1 {{unbalanced '[' character in pretty dialect name}}
func.func private @tensor_invalid_1() -> !tfr.tensor<[N, T>

// -----

// expected-error@+1 {{unbalanced}}