const auto output_type = getType(0).cast<ShapedType>();

  // Folding only implemented for float tensors.
  if (!input_type.getElementType().isF32() ||
      !weights_type.getElementType().isF32() ||
      !output_type.getElementType().isF32() ||
      (has_bias && !bias_type.getElementType().isF32())) {
    return failure();
  }

  // Folding only implemented for static shapes

  }

  return false;
}

// Returns true if the value's element type is F32.
bool IsF32Value(Value value) {
  return mlir::cast<ShapedType>(value.getType()).getElementType().isF32();
}

// Returns the number of elements in attr if it is a static shape, 1 otherwise,
// as an unranked int32 Attribute.
TypedAttr GetNumElementsOrOne(Type type) {
  auto shaped_type = mlir::cast<ShapedType>(type);

      return tflite::TensorType_UINT16;
    } else {
      return Status(absl::StatusCode::kInvalidArgument,
                    "'isSigned' can only be set for 8/16-bits integer type");
    }
  }

  if (type.isF32()) {
    return tflite::TensorType_FLOAT32;
  } else if (type.isF16()) {
    return tflite::TensorType_FLOAT16;
  } else if (type.isBF16()) {
    return tflite::TensorType_BFLOAT16;
  } else if (type.isF64()) {

  // For f32/f16 data type decision depends on the filter size in spatial
  // dimensions, for other data types we keep current data format.
  if (!input_ty.getElementType().isF32() && !input_ty.getElementType().isF16())
    return getDataFormat();

  // Keep current data format if filter rank is unknown or not equal to 4.
  auto filter_ty = mlir::dyn_cast<RankedTensorType>(getFilter().getType());

        return op.emitOpError()
               << "Missing " << attr_name << " attribute in backend_config";
      }
      auto attr = backend_config.getAs<FloatAttr>(attr_name);
      if (!attr || !attr.getType().isF32()) {
        return op.emitOpError()
               << attr_name
               << " attribute in backend_config must be of f32 type";
      }
      return success();
    };
    auto check_bool_attr =