}

  // TODO: b/202075505 - make implicit weight type clearer
  // Whether run the passes and graph rewrites for dynamic range quantization.
  bool RunAndRewriteDynamicRangeQuantizationPasses() const {
    bool dynamic_range_quantize =
        (inference_type != tensorflow::DT_FLOAT) && weight_quantization &&
        !post_training_quantization && !disable_infer_tensor_range &&

  }

  // TODO: b/176267167 - Quantize flex fallback in the MLIR pipeline
  if (quant_specs.weight_quantization &&
      (!quant_specs.RunAndRewriteDynamicRangeQuantizationPasses() ||
       !pass_config.emit_builtin_tflite_ops)) {
    // Apply post-training dynamic range quantization from the old TOCO
    // quantizer.Once MLIR has support for this, we can remove this if

      pass_manager->addNestedPass<mlir::func::FuncOp>(
          mlir::TFL::CreatePostQuantizeRemoveQDQPass());
    } else if (pass_config.quant_specs
                   .RunAndRewriteDynamicRangeQuantizationPasses()) {
      AddDynamicRangeQuantizationPasses(pass_config, *pass_manager);
    }
    pass_manager->addPass(mlir::createCanonicalizerPass());

    if (pass_config.reduce_type_precision ||