// CHECK:           %[[VAL_3:.*]] = "tfl.reshape"(%[[VAL_2]], %[[VAL_1]]) : (tensor<?x1x1x2048x!quant.uniform<i8:f32, 6.000000e-01:-128>>, tensor<2xi32>) -> tensor<?x2048x!quant.uniform<i8:f32, 6.000000e-01:-128>>
// CHECK:           return %[[VAL_3]] : tensor<?x2048x!quant.uniform<i8:f32, 6.000000e-01:-128>>

// CHECK-NEXT: %[[dq2:.*]] = "tfl.dequantize"(%[[q2]]) : (tensor<1x3x!quant.uniform<i8<-127:127>:f32:0, {{.*}}>>) -> tensor<1x3xf32>
// CHECK-NEXT: %[[q3:.*]] = "tfl.quantize"(%arg0) <{qtype = tensor<1x3x!quant.uniform<i16:f32, {{.*}}>>}> {volatile} : (tensor<1x3xf32>) -> tensor<1x3x!quant.uniform<i16:f32, {{.*}}>>

func.func @QuantizeAdd(tensor<1x56x56x24x!quant.uniform<u8:f32, 0.27583434161017922:119>>, tensor<1x56x56x24x!quant.uniform<u8:f32, 0.40149296779258581:136>>) -> tensor<1x56x56x24x!quant.uniform<u8:f32, 0.4321689530914905:133>> {
^bb0(%arg0: tensor<1x56x56x24x!quant.uniform<u8:f32, 0.27583434161017922:119>>, %arg1: tensor<1x56x56x24x!quant.uniform<u8:f32, 0.40149296779258581:136>>):

// RUN: tf-quant-opt %s -split-input-file -quant-lift-quantizable-spots-as-functions -quant-prepare-quantize-drq -quant-quantize='weight-quantization=true' -verify-each=false | FileCheck %s

// -----

module {
  func.func @matmul(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>) {
    %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32>

// CHECK: return %[[UNIFORM_QUANTIZE_1]] : tensor<1x3x!quant.uniform<i8:f32, {{.*}}>>

  %q2 = "quantfork.qcast"(%q1) {volatile} : (tensor<3x2x!quant.uniform<i8:f32, 6.000000e-03:-128>>) -> tensor<3x2x!quant.uniform<i8:f32, 0.013075299590241675:-64>>
  %dq1 = "quantfork.dcast"(%q2) : (tensor<3x2x!quant.uniform<i8:f32, 0.013075299590241675:-64>>) -> tensor<3x2xf32>

// CHECK-NEXT: [[res:%.+]] = "quantfork.dcast"([[conv]]) : (tensor<*x!quant.uniform<i8:f32, 0.023529411764705882:-128>>) -> tensor<*xf32>

// RUN: tf-quant-opt %s -split-input-file -quant-convert-fake-quant-to-qdq -quant-lift-quantizable-spots-as-functions='target-opset=XLA' -quant-insert-quantized-functions -quant-quantize-composite-functions='target-opset=XLA' -symbol-dce -inline -tf-shape-inference -canonicalize -quant-replace-cast-hacks-with-tf-xla-ops -cse -quant-optimize | FileCheck %s

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1219 : i32}, tf_saved_model.semantics} {

// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions -quant-quantize-composite-functions='quantization-method=ptq target-opset=XLA' | FileCheck %s
// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions -quant-quantize-composite-functions='quantization-method=ptq target-opset=XLA enable-per-channel-quantization=true' | FileCheck --check-prefix=PerChannel %s

module {

  func.return %0...