%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,...

  %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>>

        %4:2 = "tf.TPUReplicatedOutput"(%1) : (tensor<!tf_type.resource<tensor<i32>>>) -> (tensor<!tf_type.resource<tensor<i32>>>, tensor<!tf_type.resource<tensor<i32>>>)
        tf_executor.yield %arg0 : tensor<!tf_type.resource<tensor<i32>>>
      }
      tf_executor.fetch %outputs : tensor<!tf_type.resource<tensor<i32>>>
    }

  "tf.AssignVariableOp"(%arg0, %device_output) : (!tf_res_i32, tensor<i32>) -> ()
  "tf.SomeOp"(%device_output) : (tensor<i32>) -> ()
  func.return
}

// CHECK-LABEL: func @device_func_1
// CHECK-NOT: tf.aliasing_output
func.func @device_func_1(%arg0: tensor<i32>) -> tensor<i32> {
  func.return %arg0 : tensor<i32>
}

  %zps2 = "tf.Const"() { value = dense<2> : tensor<i32> } : () -> tensor<i32>
  %zps4 = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>
  %one = "tf.Const"() { value = dense<1> : tensor<i32> } : () -> tensor<i32>

  // CHECK: %[[QUANT0:.*]] = mhlo.uniform_quantize %[[ARG:.*]] : (tensor<2x2xf32>) -> tensor<2x2x!quant.uniform<i8:f32, 1.000000e+00:2>>

  // CHECK: "tfl.average_pool_2d"(%{{.*}}) <{filter_height = 3 : i32, filter_width = 6 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 3 : i32, stride_w = 1 : i32}> : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32>
  %0 = "tfl.average_pool_2d"(%arg0) {filter_height = 3 : i32, filter_width = 6 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 3 : i32, stride_w = 1 : i32} : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32> loc("avgpool")

  %1 = "tf.Add"(%z, %0) {__op_key = 1: i32, device = "/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %cond, %x, %y, %1 : tensor<i1>, tensor<i1>, tensor<i1>, tensor<i32>
}

// CHECK-LABEL: func @while_test_if
// CHECK-SAME: -> !tf_mlrt.tensor
func.func @while_test_if(%cond: tensor<i1>, %x: tensor<i1>, %y: tensor<i1>) -> (tensor<i32>) {

  %input_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>
  %bias_scales = "tf.Const"() { value = dense<2.0> : tensor<f32> } : () -> tensor<f32>
  %bias_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>
  %output_scales = "tf.Const"() { value = dense<2.0> : tensor<f32> } : () -> tensor<f32>
  %output_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>

  %0 = "tfl.pseudo_const"() {value = dense<[2, 2, 81]> : tensor<3xi32>} : () -> tensor<3xi32>
  %1 = "tfl.pseudo_const"() {value = dense<-1> : tensor<i32>} : () -> tensor<i32>
  %2:3 = "tfl.split_v"(%arg0, %0, %1) {num_splits = 3 : i32} : (tensor<?x13x13x85xf32>, tensor<3xi32>, tensor<i32>) -> (tensor<?x13x13x2xf32>, tensor<?x13x13x2xf32>, tensor<?x13x13x81xf32>)

  func.return %0 : tensor<f32>
}

// CHECK-LABEL: func @collective_reduce_v2_max_id
func.func @collective_reduce_v2_max_id(%input: tensor<f32>) -> tensor<f32> {
  %group_key = "tf.Const"() { value = dense<1> : tensor<i32> } : () -> tensor<i32>
  %group_size = "tf.Const"() { value = dense<2> : tensor<i32> } : () -> tensor<i32>