// CHECK: %[[dq_w:.*]] = "tfl.dequantize"(%[[q_w]]) : (tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<64x3x3x3xf32>
// CHECK: %[[emb:.*]] = "tfl.gather"(%[[dq_w]], %arg0)
// CHECK: return %[[emb:.*]]

// PerTensor: %[[q_w:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>

  %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>

    %cst_1 = "tf.Const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>
    %cst_2 = "tf.Const"() {value = dense<0.00235294132> : tensor<f32>} : () -> tensor<f32>
    %cst_3 = "tf.Const"() {value = dense<-1> : tensor<i32>} : () -> tensor<i32>
    %cst_4 = "tf.Const"() {value = dense<0.00117647066> : tensor<f32>} : () -> tensor<f32>
    %cst_5 = "tf.Const"() {value = dense<-43> : tensor<i32>} : () -> tensor<i32>

// MIN-MAX-CHECK-NEXT:  [[add:%.*]] = "tf.PartitionedCall"([[lhs]], [[rhs]])
// MIN-MAX-CHECK-NEXT:  [[res:%.*]], {{.*}}, {{.*}}, {{.*}} = "tf.CustomAggregator"([[add]]) <{calibration_method = 1 : i32, id = "composite_conv2d_with_relu6_fn_calibration_method_1", max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32, num_bins = 0 : i32}> : (tensor<*xf32>) -> (tensor<*xf32>, tensor<f32>, tensor<f32>, tensor<0xi64>)

func.func @quant_raw_data(%arg0: tensor<1x10x!quant.uniform<i8:f32, 0.1:1>>) -> tensor<1x10x!quant.uniform<i8:f32, 0.2:2>> {
  %0 = "tfr.cast"(%arg0) : (tensor<1x10x!quant.uniform<i8:f32, 0.1:1>>) -> !tfr.tensor
  %1 = tfr.quant_raw_data(%0) : (!tfr.tensor) -> !tfr.tensor
  %2 = tfr.call @tf__risc(%1) : (!tfr.tensor) -> !tfr.tensor
  %3 = "tfr.cast"(%2) : (!tfr.tensor) -> tensor<1x10x!quant.uniform<i8:f32, 0.3:3>>

  func.return %arg0 : tensor<!tf_type.resource>
}
func.func @if_else(%arg0: tensor<!tf_type.resource>, %arg1: tensor<!tf_type.resource>) -> tensor<!tf_type.resource> {
  %elem = "tf._SomeOp"() : () -> tensor<f32>
  %push = "tf.StackPushV2"(%arg1, %elem) {swap_memory = false} : (tensor<!tf_type.resource>, tensor<f32>) -> tensor<f32>
  func.return %arg1 : tensor<!tf_type.resource>
}

// -----

  %0 = "tfl.pseudo_qconst"() { qtype = tensor<3x3x!quant.uniform<i32:f32:0, {1.0, 0.5:1, 0.25:1}>>, value = dense<1> : tensor<3x3xi32>} : () -> tensor<3x3x!quant.uniform<i32:f32:0, {1.0, 0.5:1, 0.25:1}>>
  func.return %0 : tensor<3x3x!quant.uniform<i32:f32:0, {1.0, 0.5:1, 0.25:1}>>
}

func.func @qu8() -> tensor<3x!quant.uniform<u8<1:255>:f32, 1.0>> {
  // CHECK-LABEL: @qu8

    %1 = stablehlo.constant dense<1.000000e+03> : tensor<1x3xf32>
    %2:4 = "tf.CustomAggregator"(%arg0) {calibration_method = 1 : i32, id = "0", max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32, num_bins = 0 : i32} : (tensor<1x1024xf32>) -> (tensor<1x1024xf32>, tensor<f32>, tensor<f32>, tensor<*xi64>)

  %0 = "tf.Identity"(%arg0) {device = ""} : (tensor<2xf32>) -> tensor<*xf32>
  %1 = "tf.AddV2"(%0, %cst_1) {device = ""} : (tensor<*xf32>, tensor<f32>) -> tensor<*xf32>
  func.return %1 : tensor<*xf32>
// CHECK: %[[CONST_0:.*]] = "tf.Const"() <{value = dense<1.000000e-03> : tensor<f32>}> {device = ""} : () -> tensor<f32>

  // CHECK-LABEL: func @constant
  func.func @constant(%arg0: tensor<f32>) -> tensor<f32> {
    // CHECK: %[[ONE:.*]] = mhlo.constant dense<1.000000e+00> : tensor<f32>
    // CHECK: %[[RESULT:.*]] = mhlo.divide %[[ONE]], %arg0 : tensor<f32>
    // CHECK: return %[[RESULT]]

    %0 = "tf.Inv"(%arg0) : (tensor<f32>) -> tensor<f32>
    func.return %0 : tensor<f32>
  }

  // CHECK-LABEL: func @const_inputs