// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s
// Test to make sure optional parameters survive a roundtrip

func.func @main(%arg0: tensor<40x37xf32>, %arg1: tensor<40x37xf32>) -> tensor<40x40xf32> {
// CHECK: [[NONE:%.*]] = "tfl.no_value"() <{value}> : () -> none
// CHECK: "tfl.fully_connected"(%arg0, %arg1, [[NONE]])

// RUN: odml-to-stablehlo-opt %s -tfl-parse-stablehlo-ops | FileCheck -dump-input always %s

module {
    func.func @main(%arg0: tensor<8x128xf32>, %arg1: tensor<f32>) -> tensor<11x131xf32> {

// RUN: tf-reduce %s -reduction-tree='traversal-mode=0 test=%S/reducer/unsupported-op-test.sh' | FileCheck %s

// CHECK: @target_function
func.func @target_function() -> tensor<i32> {
  %0 = "tf_device.cluster"() ({
    // CHECK: tf.UnsupportedOp
    %1 = "tf.UnsupportedOp"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
    // CHECK: tf.Identity
    %2 = "tf.Identity"(%1) : (tensor<i32>) -> tensor<i32>
    // CHECK-NOT: tf.Identity

// RUN: tf-opt %s | tf-opt | FileCheck %s

// CHECK-LABEL: func @func_attr
// CHECK-SAME: tf._implements = #tf_type.func<@symbol_a, {attr0 = 1 : i32, attr1 = "random"}>
func.func @func_attr() attributes {tf._implements = #tf_type.func<@symbol_a, {attr0 = 1 : i32, attr1 = "random"}>} {
  func.return
}

// CHECK-LABEL: func @nested_func_attr

// RUN: tf-opt %s -cse  | FileCheck %s
// Checks that CSE runs without generating invalid IR and doesn't CSE ops like
// NextIteration.Source and NextIteration.Sink.
// CHECK-LABEL: func @main
// CHECK: tf_executor.NextIteration.Source
// CHECK: tf_executor.NextIteration.Source
// CHECK: tf_executor.NextIteration.Sink
// CHECK: tf_executor.NextIteration.Sink
func.func @main() -> (tensor<1xi32>, tensor<1xi32>) {
  %0, %1 = tf_executor.graph {

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

node {
  name: "x"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {
          dim {

// RUN: odml-to-stablehlo-opt %s -stablehlo-custom-call-legalize-composite | FileCheck %s

// CHECK-LABEL: module
module {
  // CHECK-LABEL: @main
  func.func @main(%arg0: tensor<1xf32>, %arg1: tensor<2xf32>) {
    // CHECK: stablehlo.custom_call @foo
    stablehlo.custom_call @foo() : () -> ()
    // CHECK-NOT: stablehlo.custom_call

// RUN: tf-mlir-translate -mlir-to-graphdef -tf-export-entry-func-to-flib  %s -o - 2>&1 | FileCheck %s

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 458 : i32}} {
  func.func @main() attributes {tf.entry_function = {inputs = "", outputs = ""}} {
    tf_executor.graph {

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s --mlir-print-debuginfo --print-after-all -tf-input-arrays=arg -tf-input-data-types=DT_INT32 -tf-input-shapes=8 -tf-output-arrays=arg -o - | FileCheck %s

node {
  name: "arg"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
versions {
  producer: 27

// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range="enable-custom-op-quantization=CustomTestOp=1" -tfl-quantize="enable-dynamic-range-quantization=true enable-custom-op-weight-only=CustomTestOp=false" -tfl-post-quantize="enable-no-side-effect=CustomTestOp=false" | FileCheck --check-prefix=NotPrune %s