# RUN: tf_tfl_translate -tf-input-arrays=input0,input1 -tf-input-shapes=4:4 -tf-input-data-types=DT_INT32,DT_INT32 -tf-output-arrays=output %s -o - --output-mlir -tf-custom-opdefs="name: 'BannaPotatoSaladWithColeslaw' input_arg: { name: 'a' type: DT_INT32 } input_arg: { name: 'b' type: DT_INT32 } output_arg: { name: 'c' type: DT_INT32 }" | FileCheck %s

node {
  name: "output"
  op: "BannaPotatoSaladWithColeslaw"
  input: "input0"
  input: "input1"
  attr {
    key: "T"

// 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"]}

node {
  name: "Add"
  op: "Add"
  input: "input0"
  input: "input1"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
}
node {
  name: "input0"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }

        void theRule(List subject, String input1, Integer input2, Set input3) {
            subject.add(input1)
            subject.add(input2)
            subject.addAll(input3)
        }
    }

    static class ArrayListRuleSource {
        @Mutate
        void theRule(ArrayList subject, String input1, Integer input2, Set input3) {
            subject.add(input1)
            subject.add(input2)
            subject.addAll(input3)

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-input-arrays=input0,input1 -tf-input-data-types=DT_INT32,DT_INT32 -tf-input-shapes=10:10 -tf-output-arrays=Sub -o - | FileCheck %s

node {
  name: "Add"
  op: "Add"
  input: "input0"
  input: "input1"
  # If device type or id doesn't exist, assign a default one (device:CPU:0).
  device: "/job:localhost/replica:0/task:0"
  attr {
    key: "T"
    value {

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

# RUN: tf_tfl_translate -tf-input-arrays=input0,input1 \
# RUN:                  -tf-input-shapes=4:4 \
# RUN:                  -tf-input-data-types=DT_FLOAT,DT_FLOAT \
# RUN:                  -tf-output-arrays=Add \
# RUN:                  -tf-inference-type=DT_QUINT8 \
# RUN:                  -tf-input-min-values='-2,-3' \
# RUN:                  -tf-input-max-values='2,3' \
# RUN:                  --quant-stats=%s.stats \

        connection.queueIncoming(input1)
        connection.queueIncoming(input2)
        connection.queueIncoming(closeInput)
        received.await()
        daemonConnection.stop()

        then:
        1 * handler.onInput(input1)
        1 * handler.onInput(input2)
        1 * handler.onEndOfInput() >> { received.countDown() }
        0 * handler._
    }

        
        input2 = getResourceFile("2.txt")
        output2 = signing.signatureType.fileFor(input2)
        input2Artifact = new DefaultPublishArtifact(input2.name, "Text File", "txt", null, null, input2)
        
        [output1, output2].each { output ->
            assert !output.exists() || output.delete()
        }
        
        assert input1.text && input2.text  // don't care what it is, just need some
    }
    

# RUN: tf_tfl_translate -tf-input-arrays=input0,input1 -tf-input-shapes=4:4 -tf-input-data-types=DT_INT32,DT_INT32 -tf-output-arrays=Add %s -o - | flatbuffer_to_string - | FileCheck %s

# Add two tensor<4xi32> inputs and return the result

node {
  name: "Add"
  op: "Add"
  input: "input0"
  input: "input1"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
}
node {
  name: "input0"
  op: "Placeholder"
  attr {