const auto float_graph = model_->subgraphs()->Get(subgraph_idx);
    // The output graph should have an extra tensor from the added dequantize
    // op.
    ASSERT_EQ(quantized_graph->tensors()->size(),
              float_graph->tensors()->size() + 1);
    // Check that a dequantize op exists.
    int32_t dequant_input_idx = -1;
    int32_t dequant_output_idx = -1;

       (TF_ConstOp (GetI64ScalarElementsAttr<-1>)))),
  (TF_SoftmaxCrossEntropyWithLogitsOp $features, $adjusted_labels)]>;

//===----------------------------------------------------------------------===//
// Dequantize op patterns.
//===----------------------------------------------------------------------===//

def DequantizeHalfRange : NativeCodeCall<
  "DequantizeHalfRange(&$_builder, $0)">;

  func.return %0 : tensor<8x8x8x8xf32>

  // CHECK-LABEL: fakeQuantArgsFalse
  // CHECK: "tfl.quantize"(%arg0) <{qtype = tensor<8x8x8x8x!quant.uniform<u8:f32, 0.0011764706057660721:85>>}>
  // CHECK: %1 = "tfl.dequantize"(%0) : (tensor<8x8x8x8x!quant.uniform<u8:f32, 0.0011764706057660721:85>>) -> tensor<8x8x8x8xf32>
}

    const mlir::TFL::PassConfig& pass_config,
    mlir::OpPassManager* pass_manager) {
  // This pass wraps all the tf.FakeQuant ops in a custom op so they are not
  // folded before being converted to tfl.quantize and tfl.dequantize ops.
  auto wrapped_ops = mlir::TFL::AllTfFakeQuantOps();
  pass_manager->addNestedPass<mlir::func::FuncOp>(
      mlir::TFL::CreateRaiseCustomOpsPass(wrapped_ops));

    ([CVE-2022-21728](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-21728))
*   Fixes a heap OOB access in `Dequantize`
    ([CVE-2022-21726](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-21726))
*   Fixes an integer overflow in shape inference for `Dequantize`
    ([CVE-2022-21727](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-21727))
*   Fixes a heap OOB access in `FractionalAvgPoolGrad`

  // The original model reshape->custom->custom->squeeze.
  ASSERT_THAT(*float_graph->operators(), SizeIs(4));
  // The resulting model should be:
  // reshape->dequantize->custom->custom->quantize->squeeze.
  ASSERT_THAT(subgraph->operators, SizeIs(6));
  const std::vector<BuiltinOperator> op_codes = {
      BuiltinOperator_RESHAPE,  BuiltinOperator_DEQUANTIZE,

// Quantization ops.
//===----------------------------------------------------------------------===//
def TFL_DequantizeOp: TFL_Op<"dequantize", [NoMemoryEffect]> {
  let summary = "Dequantize operator";

  let description = [{
    Converts quantized array of integers to floating-points according to the
    quantization parameters.
  }];

func.func @testDequantize(tensor<? x !quant.uniform<i8:f32, 0.1>>) -> tensor<? x f32> {
^bb0(%arg0: tensor<? x !quant.uniform<i8:f32, 0.1>>):
  // CHECK: "tfl.dequantize"(%arg0) : (tensor<?x!quant.uniform<i8:f32, 1.000000e-01>>) -> tensor<?xf32>
  %0 = "tfl.dequantize"(%arg0): (tensor<? x !quant.uniform<i8:f32, 0.1>>) -> tensor<? x f32>
  func.return %0 : tensor<? x f32>
}

// CHECK-LABEL: testLogicalNot

// Stores information about how to quantize a user-specified custom operation.
// CustomOpInfo contains info of its corresponding CustomOp registered in the
// CustomOpMap. 'quantizable_input_indices' is used to determine which indices
// of the CustomOp are quantizable. 'is_weight_only' is used specify whether the
// custom op is quantized only for storage and dequantized at runtime.

  let summary = "Quantizes then dequantizes a tensor.";

  let description = [{
This is almost identical to QuantizeAndDequantizeV2, except that it returns a
gradient of 1 for inputs that are within the quantization range, or 0 otherwise.
  }];

  let arguments = (ins
    Arg<TF_FloatTensor, [{Tensor to quantize and then dequantize.}]>:$input,