"tf.Yield"(%new_values#0, %new_values#1, %new_values#2) : (tensor<i32>, !tf_res,!tf_res) -> ()
        }){is_stateless = false}
        : (tensor<i32>, !tf_res, !tf_res) -> (tensor<i32>, !tf_res, !tf_res)
      tf_executor.yield
    }
    tf_executor.fetch %island : !tf_executor.control
  }
  func.return
}

// CHECK-LABEL: func @whileregion_body

      name = "avgpool", filter_height = 3 : i32, filter_width = 6 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 3 : i32, stride_w = 1 : i32
    } : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3xf32>
  %6 = "tfl.conv_2d"(%5, %4, %cst) {
      dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32

// CKECK-SAME: <{calibration_method = 5 : i32, id = "skipping_id", num_bins = 32 : i32, max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32}> : (tensor<1x3x4x3xf32>) -> (tensor<1x3x4x3xf32>, tensor<f32>, tensor<f32>, tensor<512xi64>)
// CHECK: %[[CUSTOM_AGGREGATOR_1:.*]], %[[MIN_1:.*]], %[[MAX_1:.*]], %[[HISTOGRAM_1:.*]] = "tf.CustomAggregator"

  %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>>

        %0 = "tf.Const"() {device = "", value = dense<0> : tensor<i32>} : () -> tensor<i32>
        "tf_executor.yield"(%0) : (tensor<i32>) -> ()
      }) : () -> (tensor<i32>, !tf_executor.control)
      %outputs_0:3, %control_1 = "tf_executor._SwitchN"(%outputs, %outputs) {T = i32, device = "", num_outs = 3 : i64} : (tensor<i32>, tensor<i32>) -> (tensor<*xi32>, tensor<*xi32>, tensor<*xi32>, !tf_executor.control)

// CHECK:           [[VAL_1:%.*]] = "tfl.average_pool_2d"([[VAL_0]]) <{filter_height = 48 : i32, filter_width = 48 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<1x48x48x512xf32>) -> tensor<1x1x1x512xf32>
// CHECK:           return [[VAL_1]] : tensor<1x1x1x512xf32>
// CHECK:         }

// -----

        %b = "tf.opB"(%a) : (tensor<i1>) -> tensor<i32>
        %c = "tf.opC"(%b) : (tensor<i32>) -> tensor<f32>
        tf_device.return %c, %b : tensor<f32>, tensor<i32>
      }) {device = "CPU:0"} : () -> (tensor<f32>, tensor<i32>)
      %d = "tf.opD"() : () -> tensor<i1>
      tf_executor.yield %a, %launch#0, %launch#1, %d : tensor<i1>, tensor<f32>, tensor<i32>, tensor<i1>
    }
    tf_executor.fetch
  }
  func.return
}

  %input_zp = "tf.Const"() { value = dense<-10> : tensor<i32> } : () -> tensor<i32>
  %filter_scale = "tf.Const"() { value = dense<0.5> : tensor<f32> } : () -> tensor<f32>
  %filter_zp = "tf.Const"() { value = dense<0> : tensor<i32> } : () -> tensor<i32>
  %accum_scale = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
  %accum_zp = "tf.Const"() { value = dense<0> : tensor<i32> } : () -> tensor<i32>

  %1:4 = "tf.CustomAggregator"(%arg0) {id = "1", calibration_method = 1 : i32, num_bins = 0 : i32, max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32} : (tensor<8x8x8x8xf32>) -> (tensor<8x8x8x8xf32>,...

  // CHECK: return %0
}

// CHECK-LABEL: fusedAvgPool2dRelu1
func.func @fusedAvgPool2dRelu1(%arg0: tensor<1x147x147x16xf32>) -> tensor<1x73x73x16xf32> {