}

tflite::TensorType FromTocoDataTypeToTflitToTensorType(int inference_type) {
  switch (inference_type) {
    case toco::IODataType::QUANTIZED_INT16:
      return tflite::TensorType_INT16;
    case toco::IODataType::QUANTIZED_UINT8:
      return tflite::TensorType_UINT8;
    case toco::IODataType::UINT8:
      return tflite::TensorType_UINT8;
    case toco::IODataType::QUANTIZED_INT8:

      .Case("TANH", tflite::ActivationFunctionType_TANH)
      .Case("SIGN_BIT", tflite::ActivationFunctionType_SIGN_BIT);
}

static tflite::TensorType ConvertDerivedTFLiteTypeAttrForOptionWriter(
    tflite::TensorType type, flatbuffers::FlatBufferBuilder* builder) {
  if (type == tflite::TensorType_INT64) {
    return tflite::TensorType_INT64;
  } else if (type == tflite::TensorType_INT32) {

        }
      }
    }

    os << "  tflite::" << tflite_option_name << "Builder b(*fbb);\n";
    for (const auto &option : options)
      os << formatv("  b.add_{0}(std::move({0}));\n", option);
    os << "  return b.Finish();\n}\n";
  }
}

// For each TFLite op, emits a builder function that packs the TFLite op into
// the corresponding FlatBuffer object.
//

  const ::tflite::Model* input_model = ::tflite::GetModel(buffer);

  ::tflite::optimize::BufferType quantized_type;
  switch (quant_specs.inference_type) {
    case DT_QINT8:
      quantized_type = ::tflite::optimize::BufferType::QUANTIZED_INT8;
      break;
    case DT_HALF:
      quantized_type = ::tflite::optimize::BufferType::QUANTIZED_FLOAT16;
      break;
    default:

bool IsStablehloOp(const tflite::OperatorCodeT &op_code);

// Returns the MLIR op name for the flatbuffer operator corresponding to
// `op_code`.
std::string GetMlirOpNameFromOpCode(const ::tflite::OperatorCodeT &op_code);

// Returns the builtin op code for the given MLIR operation on success; emits
// error and returns std::nullopt on failure.
std::optional<tflite::BuiltinOperator> GetBuiltinOpCode(Operation *mlir_op);

  for (auto dim : tensor.shape) {
    shape->add_dim()->set_size(int64_t{dim});
  }
  // TensorFlow Lite uses tflite::DynamicBufer to encode vector of strings.
  if (tensor.type == tflite::TensorType_STRING) {
    for (int i = 0; i < tflite::GetStringCount(buffer.data()); ++i) {
      tflite::StringRef str = tflite::GetString(buffer.data(), i);
      ret.add_string_val(str.str, str.len);
    }
    return ret;
  }

          llvm::dyn_cast_or_null<mlir::TF::TensorListReserveOp>(op)) {
    tflite::TensorType tflite_type =
        tflite::ConvertTypeToTensorType(reserve.getElementDtype());

    return CreateListReserveOptions(context, tflite_type);
  }
  if (auto empty = llvm::dyn_cast_or_null<mlir::TF::EmptyTensorListOp>(op)) {
    tflite::TensorType tflite_type =
        tflite::ConvertTypeToTensorType(empty.getElementDtype());

  }
  // The below passes only make sense if Builtin TFLite ops are enabled
  // for emission.
  if (pass_config.emit_builtin_tflite_ops) {
    // Run shape inference after variables are converted to constants.
    if (pass_config.shape_inference) {
      pass_manager->addPass(mlir::TF::CreateTFShapeInferencePass());
    }
    // Force layout supported by TFLite, this will transpose the data
    // to match 'kTFLiteDataLayout'

  let options = [
    Option<"default_min_", "default-min", "double", "-1.0",
               "Default minimum value for TFLite quantization">,
    Option<"default_max_", "default-max", "double", "1.0",
               "Default maximum value for TFLite quantization">,
    Option<"is_signed_", "is-signed", "bool", "false",
               "Is the corresponding integer signed">,
  ];
}

//
// Input: TF Saved Model or TF MLIR files.
// Output: StableHLO MLIR or TFLite Flatbuffer.
//
// Usage:
// 1) convert the input model to StableHLO MLIR
// odml_to_stablehlo /path/to/model -o=model.mlir
// 2) convert the input model to TFLite Flatbuffer
// odml_to_stablehlo /path/to/model -o=model.tflite --export-type=tflite
//
// TODO(pulkitb): Add more options.
//  * Verbose - dump intermediate translations.