func.func @while_cond(%arg0: tensor<i32>) -> tensor<i1> {
  %0 = "tf.Const"() {value = dense<9> : tensor<i32>} : () -> tensor<i32>
  %1 = "tf.Less"(%arg0, %0) {} : (tensor<i32>, tensor<i32>) -> tensor<i1>
  func.return %1 : tensor<i1>
}

func.func @while_body(%arg0: tensor<i32>) -> tensor<i32> {
  %1 = "tf.AddV2"(%arg0, %arg0) {} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %1 : tensor<i32>
}

  // CHECK-NEXT: return
  %const = "tf.Const"() {device = "/CPU:0", value = dense<0> : tensor<i32>} : () -> tensor<i32>
  %x = "tf.AddV2"(%const, %const) {device = "/CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  %r = "tf.AddV2"(%arg, %x) {device = "/CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %r : tensor<i32>
}

  func.return %0 : tensor<i32>
}

// -----

func.func @Callee(%arg0: tensor<i32>) -> tensor<i32> {
  "tf.XlaSendToHost"(%arg0) {key = "send_key_call3"} : (tensor<i32>) -> ()
  %1 = "tf.XlaRecvFromHost"() {key = "recv_key_call3", shape = #tf_type.shape<>} : () -> tensor<i32>
  func.return %1 : tensor<i32>
}

  func.return %0 : tensor<? x i32>
}

// CHECK-LABEL: testPow
func.func @testPow(tensor<? x i32>, tensor<? x i32>) -> tensor<? x i32> {
^bb0(%arg0: tensor<? x i32>, %arg1: tensor<? x i32>):
  // CHECK: tfl.pow %arg0, %arg1
  %0 = tfl.pow %arg0, %arg1 : tensor<? x i32>
  func.return %0#0 : tensor<? x i32>
}

// CHECK-LABEL: testAtan2

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    // CHECK-LABEL: func.func @main
    // CHECK-DAG: %[[CST_0:.*]] = "tf.Const"() <{value = dense<1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
    // CHECK-DAG: %[[CST_1:.*]] = "tf.Const"() <{value = dense<3> : tensor<i32>}> : () -> tensor<i32>

  %1 = "tf.ReadVariableOp"(%0) : (tensor<!tf_type.resource<tensor<i32>>>) -> tensor<i32>
  %2 = "tf_device.cluster_func"(%1) {func = @computation} : (tensor<i32>) -> tensor<i32>
  func.return %1 : tensor<i32>
}

// CHECK-LABEL: func @non_resource_read_input_write_output

  %2 = "tfl.conv_2d"(%arg0, %0, %1) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8x!quant.uniform<i8:f32, 1.0>>, tensor<16x!quant.uniform<i32:f32, 1.0>>) -> tensor<1x32x32x16x!quant.uniform<i8:f32,...

                   i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32,
                   i32, i32, i32, i32, i32, i32, i32],
      Tout = [i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32, i32,

  %2 = "tfl.pseudo_qconst"() {qtype = tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>, value = dense<0> : tensor<32xi32>} : () -> tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>

    %cst = "tf.Const"() {value = dense<127> : tensor<i32>} : () -> tensor<i32>
    %cst_0 = "tf.Const"() {value = dense<-128> : tensor<i32>} : () -> tensor<i32>
    %0 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<1x2240x2240x3xi8>) -> tensor<1x2240x2240x3xi32>