Repository: pandas-dev/pandas
File: pandas/core/arrays/numpy_.py (Python)

Code before:
        inputs = tuple(
            x._ndarray if isinstance(x, NumpyExtensionArray) else x for x in inputs
        )
        if out:
            kwargs["out"] = tuple(
                x._ndarray if isinstance(x, NumpyExtensionArray) else x for x in out
            )
        result = getattr(ufunc, method)(*inputs, **kwargs)

        if ufunc.nout > 1:
            # multiple return values; re-box array-like results
            return tuple(type(self)(x) for x in result)
        elif method == "at":
            # no return value
            return None
        elif method == "reduce":
            if isinstance(result, np.ndarray):
                # e.g. test_np_reduce_2d
                return type(self)(result)

            # e.g. test_np_max_nested_tuples
            return result
        else:
            # one return value; re-box array-like results
            return type(self)(result)

    # ------------------------------------------------------------------------
    # Pandas ExtensionArray Interface

    def astype(self, dtype, copy: bool = True):
        dtype = pandas_dtype(dtype)

        if dtype == self.dtype:
            if copy:
                return self.copy()
            return self

        result = astype_array(self._ndarray, dtype=dtype, copy=copy)
        return result

    def isna(self) -> np.ndarray:
        return isna(self._ndarray)

    def _validate_scalar(self, fill_value):
        if fill_value is None:
            # Primarily for subclasses
            fill_value = self.dtype.na_value
        return fill_value

    def _values_for_factorize(self) -> tuple[np.ndarray, float | None]:
        if self.dtype.kind in "iub":

Review comment (suggestion):
nit: Would be a nice if this was `elif` so it could be un-indented one level, but not a big deal.

Code after:

        # Defer to the implementation of the ufunc on unwrapped values.
        inputs = tuple(
            x._ndarray if isinstance(x, NumpyExtensionArray) else x for x in inputs
        )
        if out:
            kwargs["out"] = tuple(
                x._ndarray if isinstance(x, NumpyExtensionArray) else x for x in out
            )
        result = getattr(ufunc, method)(*inputs, **kwargs)

        if ufunc.nout > 1:
            # multiple return values; re-box array-like results
            return tuple(type(self)(x) for x in result)
        elif method == "at":
            # no return value
            return None
        elif method == "reduce":
            if isinstance(result, np.ndarray):
                # e.g. test_np_reduce_2d
                return type(self)(result)

            # e.g. test_np_max_nested_tuples
            return result
        else:
            if self.dtype.type is str:  # type: ignore[comparison-overlap]
                # StringDtype
                self = cast("StringArray", self)
                try:
                    # specify dtype to preserve storage/na_value
                    return type(self)(result, dtype=self.dtype)
                except ValueError:
                    # if validation of input fails (no strings)
                    # -> fallback to returning raw numpy array
                    return result
            # one return value; re-box array-like results
            return type(self)(result)

    # ------------------------------------------------------------------------
    # Pandas ExtensionArray Interface

    def astype(self, dtype, copy: bool = True):
        dtype = pandas_dtype(dtype)

        if dtype == self.dtype:
            if copy:
                return self.copy()
            return self

        result = astype_array(self._ndarray, dtype=dtype, copy=copy)
        return result