func.return %0: tensor<2x8x8xi32>
}

// -----

// CHECK-LABEL: func @selectV2BroadcastAll
func.func @selectV2BroadcastAll(%arg0: tensor<8x1x1xi1>, %arg1: tensor<1x8x1xi32>, %arg2: tensor<1x1x8xi32>) -> tensor<8x8x8xi32> {
  %0 = "tf.SelectV2"(%arg0, %arg1, %arg2) : (tensor<8x1x1xi1>, tensor<1x8x1xi32>, tensor<1x1x8xi32>) -> tensor<8x8x8xi32>
  func.return %0: tensor<8x8x8xi32>
}

// -----

  // CHECK:      %[[SLICE1:.*]] = "tf.Slice"(%arg0, %[[CST5]], %[[CST1]]) : (tensor<3x8x4xi32>, tensor<3xi64>, tensor<3xi64>) -> tensor<1x8x4xi32>
  // CHECK:      %[[CONCAT:.*]] = "tf.ConcatV2"(%[[SLICE]], %[[SLICE1]], %[[CST2]]) : (tensor<2x8x4xi32>, tensor<1x8x4xi32>, tensor<i32>) -> tensor<3x8x4xi32>

// CHECK:           [[VAL_40:%.*]] = arith.constant dense<0> : tensor<1xi32>
// CHECK:           [[VAL_41:%.*]] = "tf.ReverseV2"([[VAL_0]], [[VAL_40]]) : (tensor<8x8x8xf32>, tensor<1xi32>) -> tensor<8x8x8xf32>
// CHECK:           [[VAL_6:%.*]] = arith.constant dense<[1, 0]> : tensor<2xi32>
// CHECK:           [[VAL_7:%.*]] = "tf.Transpose"([[VAL_3]], [[VAL_6]]) : (tensor<8x40xf32>, tensor<2xi32>) -> tensor<40x8xf32>

  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>
    }) {device = "/device:CPU:0"} : () -> tensor<*xf32>

    %11 = stablehlo.convert %2 : (tensor<1x1x1xi8>) -> tensor<1x1x1xi32>
    %12 = stablehlo.broadcast_in_dim %11, dims = [0, 1, 2] : (tensor<1x1x1xi32>) -> tensor<8x16x16xi32>
    %13 = stablehlo.subtract %10, %12 : tensor<8x16x16xi32>  // q1 - z1
    %14 = stablehlo.convert %9 : (tensor<8x16x4xi8>) -> tensor<8x16x4xi32>
    %15 = stablehlo.convert %4 : (tensor<1x1x1xi8>) -> tensor<1x1x1xi32>

  %0 = "tfl.custom"(%arg0) {custom_code = "stablehlo.broadcast_in_dim", custom_option = #tfl<const_bytes : "0x62726F6164636173745F64696D656E73696F6E73000201020119010101072C022401">} : (tensor<1x2xi32>) -> tensor<1x2x2xi32>
  func.return %0 : tensor<1x2x2xi32>
  }
}

// CHECK:      module {
// CHECK-NEXT:  func @main(%arg0: tensor<1x2xi32>) -> tensor<1x2x2xi32> {

    >} : (
      tensor<2x5x6xi8>, tensor<6x8x2xi8>
    ) -> tensor<2x5x8xi32>
  %1 = chlo.broadcast_add %0, %zp_offset : (
      tensor<2x5x8xi32>, tensor<2x5x8xi32>) -> tensor<2x5x8xi32>
  %2 = chlo.broadcast_add %1, %bias : (
      tensor<2x5x8xi32>, tensor<2x5x8xi32>) -> tensor<2x5x8xi32>
  return %2 : tensor<2x5x8xi32>

// CHECK: %[[RESULT:.*]] = "tf.FloorMod"(%arg0, %arg1) : (tensor<192x8xi32>, tensor<192x8xi32>) -> tensor<192x8xi32>
// CHECK: return %[[RESULT]]
// CHECK: }
func.func @convert_floor_mod_int(%arg0: tensor<192x8xi32>, %arg1: tensor<192x8xi32>) -> tensor<192x8xi32> {
  %0 = mhlo.constant dense<0> : tensor<192x8xi32>
  %1 = mhlo.remainder %arg0, %arg1 : tensor<192x8xi32>

    }
  }
  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"

  // Use a four dimension sharding (devices=[1,1,1,1]0)
  // Since the input tensor only has three dimensions, we expect this to fail.
  %0 = "tf.XlaSharding"(%arg0) { _XlaSharding = "\08\03\1A\04\01\01\01\01\22\01\00" } : (tensor<1x2x3xi32>) -> tensor<1x2x3xi32>
  %1 = "tf.A"(%0) : (tensor<1x2x3xi32>) -> (tensor<1x2x3xi32>)
  func.return %1: tensor<1x2x3xi32>
}

// -----

// CHECK-LABEL: func @check_retval_sharding_errors