TensorFlower Gardener <******@****.***> 1672352909 -0800

---
name: TensorFlow Lite Converter Issue
about: Use this template for reporting issues during model conversion to TFLite
labels: 'TFLiteConverter'

---

### 1. System information

- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installation (pip package or built from source):
- TensorFlow library (version, if pip package or github SHA, if built from source):

### 2. Code

A. Unique TensorFlower <******@****.***> 1655263956 -0700

A. Unique TensorFlower <******@****.***> 1655263956 -0700

    case tflite::TensorType_FLOAT16:
      return builder.getF16Type();
    case tflite::TensorType_BFLOAT16:
      return builder.getBF16Type();
    case tflite::TensorType_FLOAT32:
      return builder.getF32Type();
    case tflite::TensorType_FLOAT64:
      return builder.getF64Type();
    case tflite::TensorType_INT32:
      return builder.getIntegerType(32);
    case tflite::TensorType_UINT16:

  tflite::Int32VectorT *AsInt32Vector() {
    return type == SparseIndexVector_Int32Vector ?
      reinterpret_cast<tflite::Int32VectorT *>(value) : nullptr;
  }
  const tflite::Int32VectorT *AsInt32Vector() const {
    return type == SparseIndexVector_Int32Vector ?
      reinterpret_cast<const tflite::Int32VectorT *>(value) : nullptr;
  }
  tflite::Uint16VectorT *AsUint16Vector() {

        } else {
          return tflite::TensorType_INT4;
        }
      case 8:
        return itype.isUnsigned() ? tflite::TensorType_UINT8
                                  : tflite::TensorType_INT8;
      case 16:
        return itype.isUnsigned() ? tflite::TensorType_UINT16
                                  : tflite::TensorType_INT16;
      case 32:
        return itype.isUnsigned() ? tflite::TensorType_UINT32

                             const tflite::Model* input_model,
                             BufferType quant_type,
                             bool use_updated_hybrid_scheme) {
  tflite::TensorType inference_type;
  switch (quant_type) {
    case BufferType::QUANTIZED_FLOAT16:
      inference_type = tflite::TensorType_FLOAT16;
      break;
    default:
      inference_type = tflite::TensorType_INT8;
  }

namespace mlir {
class Builder;
}  // namespace mlir

namespace tflite {
// Convert the MLIR type to the corresponding TFLite tensor.
tflite::TensorType ConvertTypeToTensorType(mlir::Type type);

// Convert the scalar type of a TFlite tensor to the corresponding MLIR type.
mlir::Type ConvertElementType(tflite::TensorType type, mlir::Builder builder);

// Convert the scalar type of a TFLite tensor to the corresponding
// Tensorflow type

}

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: