// RUN: tf-opt -split-input-file -verify-diagnostics -tf-tpu-annotate-dynamic-shape-inputs %s | FileCheck %s

// Test that annotate the inputs of the cluster func to be dynamic shaped.

module attributes {tf.devices = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:TPU_SYSTEM:0", "/job:worker/replica:0/task:0/device:TPU:0"]} {
   func.func @main(
      %arg0: tensor<2048xi64> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"},

// CHECK: func @add(
// CHECK-SAME: {tf_saved_model.index_path = ["input1"]}
// CHECK-SAME: {tf_saved_model.index_path = ["input2"]}
// CHECK-SAME: {tf_saved_model.index_path = ["result"]}
// CHECK-SAME: tf.entry_function = {inputs = "input1:0,input2:0", outputs = "result:0"}
// CHECK-SAME: tf_saved_model.exported_names = ["add"]

// CHECK: func @sub(
// CHECK-SAME: {tf_saved_model.index_path = ["input2"]}
// CHECK-SAME: {tf_saved_model.index_path = ["input1"]}

    for (int i = 0; i < num_inputs; ++i) {
      inputs_.push_back(ops::Placeholder(scope_, dtype));
    }
  }

  void CreateLoop(const ops::CondGraphBuilderFn& cond,
                  const ops::BodyGraphBuilderFn& body,
                  const std::vector<Output>* inputs = nullptr) {
    if (inputs == nullptr) inputs = &inputs_;
    TF_ASSERT_OK(ops::BuildWhileLoop(scope_, *inputs, cond, body, "test_loop",

    }

    def "can increase the quiet period"() {
        given:
        inputDir.createDir("input1")
        inputDir.createDir("input2")
        def inputFile1 = inputDir.file("input1/input.txt")
        def inputFile2 = inputDir.file("input2/input.txt")

        when:
        succeeds("theTask", "-D${StartParameterBuildOptions.ContinuousBuildQuietPeriodOption.PROPERTY_NAME}=5000")

  }

  template <typename T>
  void Run(const std::vector<Input::Initializer>& input_values,
           const std::vector<T>& expected_output_values) {
    ClientSession session(scope_);

    DCHECK_EQ(input_values.size(), inputs_.size());
    ClientSession::FeedType feeds;
    for (int i = 0; i < inputs_.size(); ++i) {
      feeds.emplace(inputs_[i], input_values[i]);
    }

    std::vector<Tensor> out_tensors;

}

Operation::Inputs Operation::GetInputs(Node* node) {
  Operation::Inputs inputs;
  if (node != nullptr) {
    inputs.resize(node->num_inputs(), {nullptr, -1});
    for (const Edge* e : node->in_edges()) {
      if (e->IsControlEdge()) continue;
      inputs[e->dst_input()] = std::make_pair(e->src(), e->src_output());
    }
  }
  return inputs;
}

Input::Initializer::Initializer(

    func.return %1 : tensor<?xf32>
  }

  func.func @sub(%arg0: tensor<?xf32> {tf_saved_model.index_path = ["input2"]}, %arg1: tensor<?xf32> {tf_saved_model.index_path = ["input1"]}) -> (tensor<?xf32> {tf_saved_model.index_path = ["result"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "input2:0,input1:0", outputs = "result:0"}, tf_saved_model.exported_names = ["sub"]} {

        return new AbstractModelAction<T>(reference, descriptor, input1, input2) {
            @Override
            public void execute(MutableModelNode modelNode, List<ModelView<?>> inputs) {
                action.execute(modelNode,
                    Cast.<I>uncheckedCast(inputs.get(0).getInstance()),
                    Cast.<J>uncheckedCast(inputs.get(1).getInstance())
                );
            }
        };
    }

# RUN: tf_tfl_translate -tf-input-arrays=input -tf-input-shapes=4 -tf-input-data-types=DT_INT32 -tf-output-arrays=output %s -o - --output-mlir | FileCheck %s

node {
  name: "default"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {
        }
        int_val: 0
      }
    }
  }
}

  std::vector<const Tensor*> inputs;
  inputs.push_back(a);
  inputs.push_back(b);
  TF_ASSERT_OK_AND_ASSIGN(auto execute_outputs,
                          RunExecutable(inputs, {}, result, executable));

  TF_EXPECT_OK(PopulateCtxOutputsFromPjRtExecutableOutputs(
      /*num_missing_prefix_ctx_inputs=*/0, inputs, {}, *result,