auto i4Values =
          tflite::UnpackDenseInt4IntoInt8(buffer, shaped_type.getNumElements());
      // Use `getFromRawBuffer()` instead of `get()` to bypass a templated size
      // check which doesn't work with int4 because int4_t doesn't exist.
      return mlir::ElementsAttr(DenseElementsAttr::getFromRawBuffer(
          shaped_type, ArrayRef<char>(i4Values)));
    }
    case 8: {
      return mlir::ElementsAttr(

    case DT_FLOAT8_E4M3FN:
      ConvertFloat8ElementsAttr<tsl::float8_e4m3fn>(
          dense_attr, output->mutable_float8_val());
      break;
    case tensorflow::DT_INT4:
      ConvertIntElementsAttr<int, tsl::int4>(dense_attr,
                                             output->mutable_int_val(),
                                             output->mutable_tensor_content());
      break;
    case tensorflow::DT_UINT4:

  // to change. Do not implement custom kernels using resource & variant types
  // now.
  RESOURCE = 13,
  VARIANT = 14,
  UINT32 = 15,
  UINT16 = 16,
  INT4 = 17,
  BFLOAT16 = 18,
}

// Custom quantization parameters for experimenting with new quantization
// techniques.
table CustomQuantization {
  custom:[ubyte] (force_align: 16);
}

    "STRING",
    "BOOL",
    "INT16",
    "COMPLEX64",
    "INT8",
    "FLOAT64",
    "COMPLEX128",
    "UINT64",
    "RESOURCE",
    "VARIANT",
    "UINT32",
    "UINT16",
    "INT4",
    "BFLOAT16",
    nullptr
  };
  return names;
}

inline const char *EnumNameTensorType(TensorType e) {
  if (::flatbuffers::IsOutRange(e, TensorType_FLOAT32, TensorType_BFLOAT16)) return "";

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