}
  quant::QuantizationSpecs quant_specs;
  quant_specs.inference_type = tflite::TflTypeToTfType(inference_type);
  quant_specs.post_training_quantization = true;
  quant_specs.disable_per_channel = disable_per_channel;
  quant_specs.disable_per_channel_for_dense_layers =
      disable_per_channel_for_dense_layers;
  quant_specs.verify_numeric = verify_numeric;
  quant_specs.whole_model_verify = whole_model_verify;

  quant::QuantizationSpecs quant_specs;
  quant_specs.inference_type = tflite::TflTypeToTfType(inference_type);
  quant_specs.weight_quantization = true;
  quant_specs.weight_only_quantization = weight_only_quantization;
  quant_specs.minimum_elements_for_weights = minimum_elements_for_weights;
  quant_specs.disable_per_channel = disable_per_channel;
  quant_specs.legacy_float_scale = legacy_float_scale;

namespace mlir {
namespace TFL {

// A config that controls which passes get run as part TFLite converter.
struct PassConfig {
  explicit PassConfig(quant::QuantizationSpecs specs)
      : quant_specs(std::move(specs)) {}

  // If `emit_builtin_tflite_ops` is true, TF Lite legalization passes will be
  // added, which produces TF Lite ops.
  bool emit_builtin_tflite_ops = true;

// input arrays.
Status PopulateQuantizationSpecs(
    const toco::ModelFlags& model_flags, toco::TocoFlags& toco_flags,
    mlir::quant::QuantizationSpecs* quant_specs,
    std::vector<string>* node_names, std::vector<string>* node_dtypes,
    std::vector<std::optional<std::vector<int>>>* node_shapes,
    std::vector<std::optional<double>>* node_mins,