# CHECK:"tf.MlirPassthroughOp"
# CHECK: mlir_module = "\0Afunc @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> {\0A %add = \22tf.Add\22(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32>\0A %ret = \22magic.op\22(%add, %add) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>\0A return %ret : tensor<10x10xf32>\0A}\0A"}> {device = ""} : (tensor<10xf32>, tensor<10xf32>) -> tensor<*xf32>

node {
  name: "x"
  op: "Placeholder"

  %0 = "tfl.unidirectional_sequence_lstm"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5, %arg6, %arg7, %arg8, %arg9, %arg10, %arg11, %arg12, %arg13, %arg14,...

  %cst = "tf.Const"() {value = dense<1.1> : tensor<100x512xf32>} : () -> tensor<100x512xf32>
  %one = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>

func.func @no_pushdown_multiple_outputs(%arg0: tensor<1000x2x!quant.uniform<i8:f32, 7.812500e-03>>) -> tensor<1000xf32> {
  %0 = "tfl.dequantize"(%arg0) : (tensor<1000x2x!quant.uniform<i8:f32, 7.812500e-03>>) -> tensor<1000x2xf32>
  %1:2 = "tfl.unpack"(%0) {axis = 1 : i32, num = 2 : i32} : (tensor<1000x2xf32>) -> (tensor<1000xf32>, tensor<1000xf32>)
  func.return %1#0 : tensor<1000xf32>

// CHECK-NEXT: tfl.dequantize
// CHECK-NEXT: tfl.unpack
}

  // CHECK-LABEL: @get_next
  func.func private @get_next(%arg0: tensor<!tf_type.resource>) -> tensor<200x10xf32> {
    // CHECK: %0 = "tf.Iterator"
    // CHECK-SAME: shared_name = "foo_iterator"
    // CHECK: "tf.IteratorGetNext"(%0)
    %0 = "tf.IteratorGetNext"(%arg0) : (tensor<!tf_type.resource>) -> tensor<200x10xf32>
    return %0 : tensor<200x10xf32>
  }

  // CHECK-LABEL: @main
  func.func @main()

func.func @float_matmul(
  %arg0: tensor<1x10xf32>, %arg1: tensor<10x10xf32>) -> (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) {
  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32>
  %0 = "tf.MatMul"(%arg0, %arg1) {
    transpose_a = false, transpose_b = false
  } : (tensor<1x10xf32>, tensor<10x10xf32>) -> tensor<*xf32>

// CHECK-NOT: call

// -----

func.func @handles_mhlo_regions() {
  "glue.someop"() : () -> ()
  %4 = mhlo.constant dense<0.0> : tensor<200x10xf32>
  %13 = mhlo.constant dense<-0.0> : tensor<f32>
  %29 = mhlo.reduce(%4 init: %13) applies mhlo.add across dimensions = [1] : (tensor<200x10xf32>, tensor<f32>) -> tensor<200xf32>
  return
}

// CHECK: func @handles_mhlo_regions
// CHECK: call [[name:@[^(]*]](

    %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32>
    %cst_0 = "tf.Const"() {value = dense<[-1, 10]> : tensor<2xi32>} : () -> tensor<2xi32>
    %1 = "tf.MatMul"(%arg0, %arg1) {
      transpose_a = false, transpose_b = false
    } : (tensor<1x10xf32>, tensor<10x10xf32>) -> tensor<1x10xf32>  loc(fused["MatMul:", "test_opt_out"])

  %cst = "tf.Const"() {value = dense<1.1> : tensor<100x512xf32>} : () -> tensor<100x512xf32>
  %one = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>

  constexpr absl::string_view kProgram = R"mlir(
func.func @main(
    %arg0: tensor<10x10xf32>, %scale: tensor<10xf32>, %zp: tensor<10xi32>
  ) -> tensor<10x10xi8> {
  %0 = "tf.UniformQuantize"(%arg0, %scale, %zp) {
    quantization_axis = 1 : i64,
    quantization_min_val = -128 : i64,
    quantization_max_val = 127 : i64
  } : (tensor<10x10xf32>, tensor<10xf32>, tensor<10xi32>) -> tensor<10x10x!tf_type.qint8>