@__forward_call_369(%arg0: tensor<16x224x224x3xf32> {tf._user_specified_name = "inputs"}, %arg1: tensor<*x!tf_type.resource>, %arg2: tensor<*x!tf_type.resource>, %arg3: 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...

// CHECK: %[[lstm:.*]] = "tfl.unidirectional_sequence_lstm"(%[[input_0]], %[[input_1]], %[[input_2]], %[[input_3]], %[[input_4]], %[[input_5]], %[[input_6]], %[[input_7]], %[[input_8]],
// CHECK-SAME: %[[input_9]], %[[input_10]], %[[input_11]], %[[input_12]], %[[input_13]], %[[input_14]], %[[input_15]], %[[input_16]], %[[input_17]], %[[input_18]], %[[input_19]],
// CHECK-SAME: %[[input_20]], %[[input_21]], %[[input_22]], %[[input_23]])

// CHECK-SAME: %[[input_0]],
// CHECK-SAME: %[[input_1]], %[[input_2]], %[[input_3]], %[[input_4]],
// CHECK-SAME: %[[input_5]], %[[input_6]], %[[input_7]], %[[input_8]],
// CHECK-SAME: %[[input_9]], %[[input_9]], %[[input_9]],
// CHECK-SAME: %[[input_10]], %[[input_11]], %[[input_12]], %[[input_13]],
// CHECK-SAME: %[[input_9]], %[[input_9]],
// CHECK-SAME: %[[input_14]], %[[input_15]],

                @InputFiles FileCollection inputs1
                @InputFiles FileCollection inputs2

                @OutputDirectory File output = project.buildDir

                @TaskAction void action() {}
            }
        """

        buildFile << """
            task test(type: TaskWithTwoFileCollectionInputs) {
                inputs1 = files("input1.txt", "input2.txt")

  std::vector<TFE_TensorHandle*> compute_fn_inputs;
  TFE_TensorHandle* input_a = TestScalarTensorHandle(ctx, 2.0f);
  TFE_TensorHandle* input_b = TestScalarTensorHandle(ctx, 1.0f);
  compute_fn_inputs.push_back(input_a);
  compute_fn_inputs.push_back(input_b);

  TFE_Op* compute_fn_op = TF_ConcreteFunctionMakeCallOp(

    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;

module {
func.func @main(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>) -> tensor<2x1xf32> attributes {tf.entry_function = {inputs = "input0,input1,input2,input3", outputs = "output"}} {
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

}

// CHECK-LABEL: func @tensor_array_while_test
// CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain
func.func @tensor_array_while_test(%indices: tensor<?xi32>, %input_0: tensor<?x?x?xf32>, %input_1: tensor<?x?x?xf32>) -> (tensor<?x?x512xf32>, tensor<?x?x512xf32>) {
  %index = "tf.Const"() {device = "/device:CPU:0", value = dense<0> : tensor<i32>} : () -> (tensor<i32>)

              Eq(TensorType_INT8));
  ASSERT_THAT(float_graph->tensors()->Get(float_op->inputs()->Get(1))->type(),
              Eq(TensorType_FLOAT32));
  EXPECT_THAT(subgraph->tensors[op->inputs[1]].get()->type,
              Eq(TensorType_INT8));

  // Verify FC bias should be int32 quantized.
  ASSERT_THAT(float_graph->tensors()->Get(float_op->inputs()->Get(2))->type(),

              NodeWith(Op("Add"), Name("add"),
                       Inputs(Out(NodeWith(Name("placeholder_a"))),
                              Out(NodeWith(Name("placeholder_b"))))));

  EXPECT_EQ(Explain(add.node(), NodeWith(Inputs())),
            "\nexpected 0 inputs but node has 2");
  EXPECT_EQ(
      Explain(add.node(), NodeWith(Inputs(Out(NodeWith(Name("blah"))), _))),