}
  }
  experimental_debug_info {
  }
}
library {
}
versions {
  producer: 198
}

# CHECK-LABEL: func @main
# CHECK-SAME:  ([[VAL_0:%.*]]: tensor<1x8x8x2xi32>) -> (tensor<1x8x8x2xi32>, tensor<i32>, tensor<10xi32>)
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "unranked"
# CHECK-SAME:  outputs = "unranked,static,static_10"

    %12 = "tf.AddV2"(%11, %arg10) : (tensor<1x3x2x2xi32>, tensor<i32>) -> tensor<1x3x2x2xi32>
    %13 = "tf.Maximum"(%cst_0, %arg10) : (tensor<i32>, tensor<i32>) -> tensor<i32>
    %14 = "tf.ClipByValue"(%12, %13, %cst) : (tensor<1x3x2x2xi32>, tensor<i32>, tensor<i32>) -> tensor<1x3x2x2xi32>
    %15 = "tf.Cast"(%14) {Truncate = false} : (tensor<1x3x2x2xi32>) -> tensor<1x3x2x2xi8>

func.func @infeed_dequeue_tuple() -> (tensor<1x8x4x4xi32>, tensor<1x100x1xf32>) {
  // CHECK: [[TOKEN:%.*]] = mhlo.create_token : !mhlo.token
  %0 = "mhlo.create_token"() : () -> !mhlo.token

  // CHECK:               [[INFEED:%.*]]:3 = "mhlo.infeed"([[TOKEN]]) <{
  // CHECK-SAME{LITERAL}:   infeed_config = "", layout = [[1, 3, 2, 0], [1, 2, 0]]
  // CHECK-SAME:          }> : (!mhlo.token) -> (tensor<1x8x4x4xi32>, tensor<1x100x1xf32>, !mhlo.token)

// CHECK: %[[pad:.*]] = "tf.PadV2"(%[[quant]]
// CHECK: %[[xlaconv:.*]] = "tf.XlaConvV2"(%[[pad]]
// CHECK: %[[sub:.*]] = "tf.Sub"(%[[xlaconv]]
// CHECK: %[[cast:.*]] = "tf.Cast"(%[[sub]]) <{Truncate = false}> : (tensor<1x3x2x2xi32>) -> tensor<1x3x2x2xf32>
// CHECK: %[[dequant1:.*]] = "tf.Mul"(%[[cast]]
// CHECK: %[[relu:.*]] = "tf.Relu"(%[[dequant1]]
// CHECK: %[[clamped:.*]] = "tf.Minimum"(%[[relu]]

// CHECK: %[[rescale1:.*]] = "tf.Mul"(%[[cast]]

// permutated to match the new input shape.
// CHECK: (tensor<1x16x16x4xf32>, tensor<f32>) -> tensor<1x8x8x4xf32>

// Check that a `stablehlo.transpose` is added to the result to match the shape
// of the users.
// CHECK: %[[TRANSPOSE:.+]] = stablehlo.transpose %[[REDUCE_WINDOW]], dims = [0, 3, 1, 2] : (tensor<1x8x8x4xf32>) -> tensor<1x4x8x8xf32>
// CHECK: return %[[TRANSPOSE]]

// -----

// CHECK-DAG: %[[CONST_1:.+]] = stablehlo.constant() <{value = dense<{{.*}}> : tensor<1x1x1x2xi32>}> : () -> tensor<1x1x1x2x!quant.uniform<i32:f32:3, {{.*}}>
// CHECK: %[[UNIFORM_QUANTIZE_0:.+]] = stablehlo.uniform_quantize %[[ARG_0]] : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3x!quant.uniform<i8:f32, {{.*}}>>

func.func @invalidSelect(%arg0: tensor<1x8xi1>, %arg1: tensor<1x8x8xi32>, %arg2: tensor<1x8x8xi32>) -> tensor<1x8x8xi32> {
  // expected-error @+1 {{requires that pred is a scalar OR has the same rank as t and e OR is a vector}}
  %0 = "tf.Select"(%arg0, %arg1, %arg2) : (tensor<1x8xi1>, tensor<1x8x8xi32>, tensor<1x8x8xi32>) -> tensor<1x8x8xi32>
  func.return %0: tensor<1x8x8xi32>
}

// -----

  }

  // CHECK-LABEL: infer_device_launch
  func.func @infer_device_launch(%arg0: tensor<1x8x2xi32>) -> (tensor<*xf32>, tensor<*xf32>) {
    %0 = "tf.Const"() {value = dense<-1> : tensor<i32>} : () -> tensor<i32>
    %1 = "tf_device.launch"() ({
      %2 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<1x8x2xi32>) -> tensor<1x8x2xf32>
      tf_device.return %2 : tensor<1x8x2xf32>
    // CHECK: () -> tensor<1x8x2xf32>

}

func.func @topk_4(%arg0: tensor<1x2x3x4xf32>) -> (tensor<1x2x3x2xf32>, tensor<1x2x3x2xi32>) {
  %0 = "tf.Const"() { value = dense<2> : tensor<i32> } : () -> tensor<i32>
  %1:2 = "tf.TopKV2"(%arg0, %0) : (tensor<1x2x3x4xf32>, tensor<i32>) -> (tensor<1x2x3x2xf32>, tensor<1x2x3x2xi32>)
  func.return %1#0, %1#1: tensor<1x2x3x2xf32>, tensor<1x2x3x2xi32>

// CHECK-LABEL: topk_4
// CHECK:  "tfl.topk_v2"(%arg0, %cst)
// CHECK:  return

    %0 = stablehlo.constant() {value = dense<3> : tensor<3x3x3x2xi8>} : () -> tensor<3x3x3x2x!quant.uniform<i8:f32:3, {3.000000e+00, 3.000000e+00}>>
    %1 = stablehlo.constant() {value = dense<5> : tensor<1x1x1x2xi32>} : () -> tensor<1x1x1x2x!quant.uniform<i32:f32:3, {2.000000e+00, 2.000000e+00}>>