// CHECK: %[[ADD_0:.+]] = stablehlo.add %[[DOT_GENERAL_0]], %[[DYNAMIC_BROADCAST_IN_DIM_0]] : tensor<?x3x!quant.uniform<i32:f32:1, {{.*}}>>
// CHECK: %[[UNIFORM_QUANTIZE_1:.+]] = stablehlo.uniform_quantize %[[ADD_0]] : (tensor<?x3x!quant.uniform<i32:f32:1, {{.*}}>>)

  }

  func.func @while_cond_lt9(%arg0: tensor<i32>) -> tensor<i1> {
    %0 = "tf.Const"() {device = "/device:CPU:0", value = dense<9> : tensor<i32>} : () -> tensor<i32>
    %1 = "tf.Less"(%arg0, %0) {device = "/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i1>
    func.return %1 : tensor<i1>
  }

  func.func @while_body_add2(%arg0: tensor<i32>) -> tensor<i32> {

// CKECK-SAME: <{calibration_method = 5 : i32, id = "0", 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"

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main(%arg0 : tensor<?xf32>) -> tensor<?xf32> {
      %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
      %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

      %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {

  %1 = "tfl.max_pool_2d"(%0) {filter_height = 1 : i32, filter_width = 1 : i32, fused_activation_function = "RELU6", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32>
  func.return %1 : tensor<1x1x1x16xf32>

// MIN-MAX-CHECK-NEXT:  [[lhs:%.*]], {{.*}}, {{.*}}, {{.*}} = "tf.CustomAggregator"(%arg0) <{calibration_method = 1 : i32, id = "composite_conv2d_with_relu6_fn_arg_0_calibration_method_1",...

func.func @pseudo_const() -> tensor<i32> {
  // CHECK: %[[CST:.*]] = arith.constant dense<1> : tensor<i32>
  // CHECK: return %[[CST]]
  %0 = "tfl.pseudo_const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  func.return %0 : tensor<i32>
}


// CHECK-LABEL: @range_int
func.func @range_int() -> tensor<?xi32> {
  %cst = arith.constant dense<0> : tensor<i32>
  %cst_1 = arith.constant dense<4> : tensor<i32>

// CHECK:           %[[VAL_6:.*]] = "tf.Minimum"(%[[VAL_4]], %[[VAL_5]]) : (tensor<i32>, tensor<i32>) -> tensor<i32>
// CHECK:           %[[VAL_7:.*]] = "tf.Maximum"(%[[VAL_6]], %[[VAL_3]]) : (tensor<i32>, tensor<i32>) -> tensor<i32>
// CHECK:           %[[VAL_8:.*]] = "tf.Cast"(%[[VAL_2]]) <{Truncate = false}> : (tensor<i32>) -> tensor<i32>
// CHECK:           %[[VAL_9:.*]] = "tf.Const"() <{value = dense<1> : tensor<i32>}> : () -> tensor<i32>

  // CHECK-SAME: tfl.schema_version = 3 : i32

  // CHECK:          %{{.*}} = "tfl.pseudo_const"() <{value = dense<{{\[\[1, 2\], \[3, 4\], \[5, 6\]\]}}> : tensor<3x2xi32>}>
  // CHECK-NEXT:     [[SUB:%.*]] = tfl.sub %{{.*}}, %{{.*}} {fused_activation_function = "RELU6"} : tensor<3x2xi32>
  // CHECK-NEXT:     [[SCALAR:%.*]] = "tfl.pseudo_const"() <{value = dense<10> : tensor<i32>}> : () -> tensor<i32>

  %conv = "tfl.conv_2d"(%0, %w, %b) {
    dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU",
    padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32
  } : (tensor<1x5x5x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<1x5x5x3xf32>
  %conv2 = "tfl.conv_2d"(%0, %w, %b2) {
    dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU",