func.func @main(%arg0: tensor<10xi32>) -> tensor<10xi32>
attributes {tf.entry_function = {inputs = "input0", outputs = "output0"}} {
  %graph = tf_executor.graph {
    tf_executor.fetch %arg0 : tensor<10xi32>
  }
  func.return %graph : tensor<10xi32>
}

// CHECK:      node {
// CHECK-NEXT:   name: "input0"
// CHECK-NEXT:   op: "_Arg"
// CHECK:          key: "T"
// CHECK-NEXT:     value {

@__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]],

%9 = "tf.opJ"() : () -> tensor<i1>
%output:8 = tf_device.replicate([%0, %1] as %input_0: tensor<i32>,
                                [%2, %3] as %input_1: tensor<f32>,
                                [%4, %5] as %input_2: tensor<!tf_type.resource>,
                                [%6, %7] as %input_3: tensor<!tf_type.string>,
                                %8 as %input_4: tensor<!tf_type.variant>)
                {n = 2 : i32,

    RunOptions run_options;

    Tensor input_a = CreateInputTensor(shape, 0);
    Tensor input_b = CreateInputTensor(shape, shape.num_elements());
    Tensor input_c = CreateInputTensor(shape, 2 * shape.num_elements());
    TF_RETURN_IF_ERROR(session->Run(
        run_options,
        {std::make_pair("a", input_a), std::make_pair("b", input_b),
         std::make_pair("c", input_c)},
        {"m"}, {}, &golden_output_tensors, nullptr));

                @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")

  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
  Output input_a = ops::Placeholder(s.WithOpName("input_a"), DT_INT32,
                                    ops::Placeholder::Attrs{});
  Output input_b = ops::Placeholder(s.WithOpName("shape_b"), DT_FLOAT,
                                    ops::Placeholder::Attrs{});
  Output mul = ops::Mul(s.WithOpName("mul"), input_b, input_b);
  Output shape_of_mul = ops::Shape(s.WithOpName("shape_of_mul"), mul);

    key: "num_outs"
    value {
      i: 3
    }
  }
}
node {
  name: "Case/Case/input_0/_7"
  op: "_SwitchN"
  input: "Case/input_0"
  input: "Case/branch_index"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "Case/input_0"
      }
    }
  }
  attr {
    key: "num_outs"
    value {
      i: 2
    }

      : inputs_(inputs.begin(), inputs.end()) {}

  InputList(
      const std::initializer_list<Output>& out) {  // NOLINT(runtime/explicit)
    for (auto const& x : out) {
      inputs_.push_back(x);
    }
  }

  typename std::vector<Input>::iterator begin() { return inputs_.begin(); }
  typename std::vector<Input>::iterator end() { return inputs_.end(); }