%0 = tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "tf.Const"() {device = "", dtype = f32, value = dense<[]> : tensor<0xf32>} : () -> tensor<0xf32>
      %outputs_0, %control_1 = tf_executor.island wraps "tf.Const"() {device = "", dtype = f32, value = dense<[]> : tensor<0xf32>} : () -> tensor<0xf32>

  }

  // CHECK-LABEL: pack
  func.func @pack(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<2x1xf32> {
    %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>
    func.return %0 : tensor<2x1xf32>
    // CHECK: %[[VAL_0:.*]] = arith.constant dense<[2, 1]> : tensor<2xi32>

  %dense_default_0 = "tf.Const"() {device = "/device:CPU:0", dtype = f32, value = dense<[]> : tensor<0xf32>} : () -> tensor<0xf32>
  %dense_default_1 = "tf.Const"() {device = "/device:CPU:0", dtype = f32, value = dense<[]> : tensor<0xf32>} : () -> tensor<0xf32>
  %dense_keys = "tf.Const"() {device = "/device:CPU:0", dtype = !tf_type.string, value = dense<""> : tensor<2x!tf_type.string>} : () -> tensor<2x!tf_type.string>

  %0 = "quantfork.stats"(%arg0) {bitsNum = 8 : i64, layerStats = dense<[-0.999415695, 0.99998933]> : tensor<2xf32>, narrowRange = false} : (tensor<?x3xf32>) -> tensor<?x3xf32>
  // CHECK: %[[dot:.*]] = stablehlo.dot %[[dq2]], %[[dq1]]
  %1 = stablehlo.dot %0, %cst : (tensor<?x3xf32>, tensor<3x2xf32>) -> tensor<?x2xf32>
  // CHECK: %[[q3:.*]] = "quantfork.qcast"(%[[dot]])
  // CHECK-SAME: quant.uniform<i4:f32, 0.61633392969767253:-2>>

// CHECK:         }
func.func @tfl_wrapped_jax_random_uniform(%arg0: tensor<2xui32>) -> tuple<tensor<1x2xf32>> {
  // This is a fake jax random uniform body.
  %0 = stablehlo.constant dense<0.0> : tensor<2xf32>
  %1 = "stablehlo.reshape"(%0) : (tensor<2xf32>) -> tensor<1x2xf32>
  %2 = "stablehlo.tuple"(%1) : (tensor<1x2xf32>) -> tuple<tensor<1x2xf32>>
  func.return %2 : tuple<tensor<1x2xf32>>

  %4 = "tfr.cast"(%concat) : (!tfr.tensor) -> tensor<3xf32>
  func.return %4 : tensor<3xf32>

// CHECK: %[[concat:.*]] = "tf.RiscConcat"(%arg0, %arg1, %arg2) : (tensor<f32>, tensor<f32>, tensor<f32>) -> tensor<*xf32>
// CHECK: %[[es:.*]] = "tf.EnsureShape"(%[[concat]]) <{shape = #tf_type.shape<3>}> : (tensor<*xf32>) -> tensor<3xf32>
// CHECK: return %[[es]] : tensor<3xf32>
}

// CHECK-LABEL: decompose_tf_split

      func.return %arg0 : tensor<?xi32>
    }

    func.func @main(%arg0: tensor<20xi32>, %arg1: tensor<?xi32>) -> tensor<*xi32> {
      %2 = "tf.TPUPartitionedCall"(%arg0, %arg1) {f = @tpu_partitioned_call_func} : (tensor<20xi32>, tensor<?xi32>) -> tensor<*xi32>
      func.return %2 : tensor<*xi32>
    }
  })";

  CellReader<int64_t> error(kHasTpuPartitionedCallStreamzName);

func.func private @simple_callee_with_devices() -> (tensor<2xi32>, tensor<2xf32>)  {
  %cst = "tf.Const"() { value = dense<2> : tensor<2xi32> } : () -> tensor<2xi32>
  %cst_gpu = "tf.Const"() { value = dense<2.> : tensor<2xf32>, device = "/GPU:0" } : () -> tensor<2xf32>
  func.return %cst, %cst_gpu : tensor<2xi32>, tensor<2xf32>
}
// CHECK-LABEL: func @inline_simple_with_devices(

  %2 = "tf.Conv2D"(%1, %0) {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
  %3 = "tf.BiasAdd"(%2, %cst) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32>

  %0 = mhlo.constant dense<0.000000e+00> : tensor<32x10xf32>
  %1 = mhlo.constant dense<0.000000e+00> : tensor<32x28x28x5xf32>
  %2 = "tf.ReadVariableOp"(%arg4) : (tensor<!tf_type.resource<tensor<10xf32>>>) -> tensor<10xf32>