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;

    ([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,

// 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,

  pm.addPass(TFL::CreatePostQuantizeRemoveQDQPass());
  if (failed(pm.run(module.get()))) {
    const std::string err(statusHandler.ConsumeStatus().message());
    LOG(ERROR) << "Failed to quantize: " << err;
    return kTfLiteError;
  }

  // Export the results.
  tflite::FlatbufferExportOptions options;
  options.toco_flags.set_force_select_tf_ops(false);

      mlir::TFL::PassConfig(quant_specs), pm);

  if (failed(pm.run(module.get()))) {
    absl::string_view err = statusHandler.ConsumeStatus().message();
    LOG(ERROR) << "Failed to quantize: " << err;
    return kTfLiteError;
  }

  // Export the results to the builder
  std::string result;
  tflite::FlatbufferExportOptions options;
  options.toco_flags.set_force_select_tf_ops(false);

        "transforms/post_quantize.cc",
        "transforms/prepare_quantize.cc",
        "transforms/prepare_quantize_dynamic_range.cc",
        "transforms/prepare_quantize_helper.cc",
        "transforms/quantize.cc",
        "transforms/quantize_variables.cc",
        "utils/generated_op_quant_spec_getters.inc",
    ],
    hdrs = [
        "transforms/passes.h",
        "transforms/prepare_quantize_helper.h",
    ],