def _convert(image, dtype, force_copy=False, uniform=False):
    """
    Adapted from: https://github.com/scikit-image/scikit-image/blob/main/skimage/util/dtype.py#L510-L531

    Convert an image to the requested data-type.
    Warnings are issued in case of precision loss, or when negative values
    are clipped during conversion to unsigned integer types (sign loss).
    Floating point values are expected to be normalized and will be clipped
    to the range [0.0, 1.0] or [-1.0, 1.0] when converting to unsigned or
    signed integers respectively.
    Numbers are not shifted to the negative side when converting from
    unsigned to signed integer types. Negative values will be clipped when
    converting to unsigned integers.
    Parameters
    ----------
    image : ndarray
        Input image.
    dtype : dtype
        Target data-type.
    force_copy : bool, optional
        Force a copy of the data, irrespective of its current dtype.
    uniform : bool, optional
        Uniformly quantize the floating point range to the integer range.
        By default (uniform=False) floating point values are scaled and
        rounded to the nearest integers, which minimizes back and forth
        conversion errors.
    .. versionchanged :: 0.15
        ``_convert`` no longer warns about possible precision or sign
        information loss. See discussions on these warnings at:
        https://github.com/scikit-image/scikit-image/issues/2602
        https://github.com/scikit-image/scikit-image/issues/543#issuecomment-208202228
        https://github.com/scikit-image/scikit-image/pull/3575
    References
    ----------
    .. [1] DirectX data conversion rules.
           https://msdn.microsoft.com/en-us/library/windows/desktop/dd607323%28v=vs.85%29.aspx
    .. [2] Data Conversions. In "OpenGL ES 2.0 Specification v2.0.25",
           pp 7-8. Khronos Group, 2010.
    .. [3] Proper treatment of pixels as integers. A.W. Paeth.
           In "Graphics Gems I", pp 249-256. Morgan Kaufmann, 1990.
    .. [4] Dirty Pixels. J. Blinn. In "Jim Blinn's corner: Dirty Pixels",
           pp 47-57. Morgan Kaufmann, 1998.
    """
    dtype_range = {
        bool: (False, True),
        np.bool_: (False, True),
        float: (-1, 1),
        np.float16: (-1, 1),
        np.float32: (-1, 1),
        np.float64: (-1, 1),
    }

    if hasattr(np, "float_"):
        dtype_range[np.float_] = dtype_range[float]  # type: ignore
    if hasattr(np, "bool8"):
        dtype_range[np.bool8] = dtype_range[np.bool_]  # type: ignore

    def _dtype_itemsize(itemsize, *dtypes):
        """Return first of `dtypes` with itemsize greater than `itemsize`
        Parameters
        ----------
        itemsize: int
            The data type object element size.
        Other Parameters
        ----------------
        *dtypes:
            Any Object accepted by `np.dtype` to be converted to a data
            type object
        Returns
        -------
        dtype: data type object
            First of `dtypes` with itemsize greater than `itemsize`.
        """
        return next(dt for dt in dtypes if np.dtype(dt).itemsize >= itemsize)

    def _dtype_bits(kind, bits, itemsize=1):
        """Return dtype of `kind` that can store a `bits` wide unsigned int
        Parameters:
        kind: str
            Data type kind.
        bits: int
            Desired number of bits.
        itemsize: int
            The data type object element size.
        Returns
        -------
        dtype: data type object
            Data type of `kind` that can store a `bits` wide unsigned int
        """

        s = next(
            i
            for i in (itemsize,) + (2, 4, 8)
            if bits < (i * 8) or (bits == (i * 8) and kind == "u")
        )

        return np.dtype(kind + str(s))

    def _scale(a, n, m, copy=True):
        """Scale an array of unsigned/positive integers from `n` to `m` bits.
        Numbers can be represented exactly only if `m` is a multiple of `n`.
        Parameters
        ----------
        a : ndarray
            Input image array.
        n : int
            Number of bits currently used to encode the values in `a`.
        m : int
            Desired number of bits to encode the values in `out`.
        copy : bool, optional
            If True, allocates and returns new array. Otherwise, modifies
            `a` in place.
        Returns
        -------
        out : array
            Output image array. Has the same kind as `a`.
        """
        kind = a.dtype.kind
        if n > m and a.max() < 2**m:
            return a.astype(_dtype_bits(kind, m))
        elif n == m:
            return a.copy() if copy else a
        elif n > m:
            # downscale with precision loss
            if copy:
                b = np.empty(a.shape, _dtype_bits(kind, m))
                np.floor_divide(a, 2 ** (n - m), out=b, dtype=a.dtype, casting="unsafe")
                return b
            else:
                a //= 2 ** (n - m)
                return a
        elif m % n == 0:
            # exact upscale to a multiple of `n` bits
            if copy:
                b = np.empty(a.shape, _dtype_bits(kind, m))
                np.multiply(a, (2**m - 1) // (2**n - 1), out=b, dtype=b.dtype)
                return b
            else:
                a = a.astype(_dtype_bits(kind, m, a.dtype.itemsize), copy=False)
                a *= (2**m - 1) // (2**n - 1)
                return a
        else:
            # upscale to a multiple of `n` bits,
            # then downscale with precision loss
            o = (m // n + 1) * n
            if copy:
                b = np.empty(a.shape, _dtype_bits(kind, o))
                np.multiply(a, (2**o - 1) // (2**n - 1), out=b, dtype=b.dtype)
                b //= 2 ** (o - m)
                return b
            else:
                a = a.astype(_dtype_bits(kind, o, a.dtype.itemsize), copy=False)
                a *= (2**o - 1) // (2**n - 1)
                a //= 2 ** (o - m)
                return a

    image = np.asarray(image)
    dtypeobj_in = image.dtype
    dtypeobj_out = (
        dtypeobj_in
        if dtype is np.floating
        else np.dtype("float64")
        if dtype is float
        else np.dtype(dtype)
    )
    dtype_in = dtypeobj_in.type
    dtype_out = dtypeobj_out.type
    kind_in = dtypeobj_in.kind
    kind_out = dtypeobj_out.kind
    itemsize_in = dtypeobj_in.itemsize
    itemsize_out = dtypeobj_out.itemsize

    # Below, we do an `issubdtype` check.  Its purpose is to find out
    # whether we can get away without doing any image conversion.  This happens
    # when:
    #
    # - the output and input dtypes are the same or
    # - when the output is specified as a type, and the input dtype
    #   is a subclass of that type (e.g. `np.floating` will allow
    #   `float32` and `float64` arrays through)

    if hasattr(np, "obj2sctype"):
        is_subdtype = np.issubdtype(dtype_in, np.obj2sctype(dtype))  # type: ignore
    else:
        is_subdtype = np.issubdtype(dtype_in, dtypeobj_out.type)

    if is_subdtype:
        if force_copy:
            image = image.copy()
        return image

    if kind_in in "ui":
        imin_in = np.iinfo(dtype_in).min
        imax_in = np.iinfo(dtype_in).max
    if kind_out in "ui":
        imin_out = np.iinfo(dtype_out).min  # type: ignore
        imax_out = np.iinfo(dtype_out).max  # type: ignore

    # any -> binary
    if kind_out == "b":
        return image > dtype_in(dtype_range[dtype_in][1] / 2)

    # binary -> any
    if kind_in == "b":
        result = image.astype(dtype_out)
        if kind_out != "f":
            result *= dtype_out(dtype_range[dtype_out][1])
        return result

    # float -> any
    if kind_in == "f":
        if kind_out == "f":
            # float -> float
            return image.astype(dtype_out)

        if np.min(image) < -1.0 or np.max(image) > 1.0:
            raise ValueError("Images of type float must be between -1 and 1.")
        # floating point -> integer
        # use float type that can represent output integer type
        computation_type = _dtype_itemsize(
            itemsize_out, dtype_in, np.float32, np.float64
        )

        if not uniform:
            if kind_out == "u":
                image_out = np.multiply(image, imax_out, dtype=computation_type)  # type: ignore
            else:
                image_out = np.multiply(
                    image,
                    (imax_out - imin_out) / 2,  # type: ignore
                    dtype=computation_type,
                )
                image_out -= 1.0 / 2.0
            np.rint(image_out, out=image_out)
            np.clip(image_out, imin_out, imax_out, out=image_out)  # type: ignore
        elif kind_out == "u":
            image_out = np.multiply(image, imax_out + 1, dtype=computation_type)  # type: ignore
            np.clip(image_out, 0, imax_out, out=image_out)  # type: ignore
        else:
            image_out = np.multiply(
                image,
                (imax_out - imin_out + 1.0) / 2.0,  # type: ignore
                dtype=computation_type,
            )
            np.floor(image_out, out=image_out)
            np.clip(image_out, imin_out, imax_out, out=image_out)  # type: ignore
        return image_out.astype(dtype_out)

    # signed/unsigned int -> float
    if kind_out == "f":
        # use float type that can exactly represent input integers
        computation_type = _dtype_itemsize(
            itemsize_in, dtype_out, np.float32, np.float64
        )

        if kind_in == "u":
            # using np.divide or np.multiply doesn't copy the data
            # until the computation time
            image = np.multiply(image, 1.0 / imax_in, dtype=computation_type)  # type: ignore
            # DirectX uses this conversion also for signed ints
            # if imin_in:
            #     np.maximum(image, -1.0, out=image)
        else:
            image = np.add(image, 0.5, dtype=computation_type)
            image *= 2 / (imax_in - imin_in)  # type: ignore

        return np.asarray(image, dtype_out)

    # unsigned int -> signed/unsigned int
    if kind_in == "u":
        if kind_out == "i":
            # unsigned int -> signed int
            image = _scale(image, 8 * itemsize_in, 8 * itemsize_out - 1)
            return image.view(dtype_out)
        else:
            # unsigned int -> unsigned int
            return _scale(image, 8 * itemsize_in, 8 * itemsize_out)

    # signed int -> unsigned int
    if kind_out == "u":
        image = _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out)
        result = np.empty(image.shape, dtype_out)
        np.maximum(image, 0, out=result, dtype=image.dtype, casting="unsafe")
        return result

    # signed int -> signed int
    if itemsize_in > itemsize_out:
        return _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out - 1)

    image = image.astype(_dtype_bits("i", itemsize_out * 8))
    image -= imin_in  # type: ignore
    image = _scale(image, 8 * itemsize_in, 8 * itemsize_out, copy=False)
    image += imin_out  # type: ignore
    return image.astype(dtype_out)

    Adapted from: https://github.com/scikit-image/scikit-image/blob/main/skimage/util/dtype.py#L510-L531

    Convert an image to the requested data-type.
    Warnings are issued in case of precision loss, or when negative values
    are clipped during conversion to unsigned integer types (sign loss).
    Floating point values are expected to be normalized and will be clipped
    to the range [0.0, 1.0] or [-1.0, 1.0] when converting to unsigned or
    signed integers respectively.
    Numbers are not shifted to the negative side when converting from
    unsigned to signed integer types. Negative values will be clipped when
    converting to unsigned integers.
    Parameters
    ----------
    image : ndarray
        Input image.
    dtype : dtype
        Target data-type.
    force_copy : bool, optional
        Force a copy of the data, irrespective of its current dtype.
    uniform : bool, optional
        Uniformly quantize the floating point range to the integer range.
        By default (uniform=False) floating point values are scaled and
        rounded to the nearest integers, which minimizes back and forth
        conversion errors.
    .. versionchanged :: 0.15
        ``_convert`` no longer warns about possible precision or sign
        information loss. See discussions on these warnings at:
        https://github.com/scikit-image/scikit-image/issues/2602
        https://github.com/scikit-image/scikit-image/issues/543#issuecomment-208202228
        https://github.com/scikit-image/scikit-image/pull/3575
    References
    ----------
    .. [1] DirectX data conversion rules.
           https://msdn.microsoft.com/en-us/library/windows/desktop/dd607323%28v=vs.85%29.aspx
    .. [2] Data Conversions. In "OpenGL ES 2.0 Specification v2.0.25",
           pp 7-8. Khronos Group, 2010.
    .. [3] Proper treatment of pixels as integers. A.W. Paeth.
           In "Graphics Gems I", pp 249-256. Morgan Kaufmann, 1990.
    .. [4] Dirty Pixels. J. Blinn. In "Jim Blinn's corner: Dirty Pixels",
           pp 47-57. Morgan Kaufmann, 1998.
    
gradio/processing_utils.py