How to use the aicsimageio.constants.Dimensions.SpatialY function in aicsimageio

def _daread_safe(
        img: Union[str, Path],
        chunk_by_dims: List[str] = [
            Dimensions.SpatialZ,
            Dimensions.SpatialY,
            Dimensions.SpatialX,
        ],
        S: int = 0,
    ) -> Tuple[da.core.Array, str]:
        """
        Safely read a CZI image file as a delayed dask array where certain dimensions
        act as the chunk size.

        Parameters
        ----------
        img: Union[str, Path]
            The filepath to read.
        chunk_by_dims: List[str]
            The dimensions to use as the for mapping the chunks / blocks.
            Default: [Dimensions.SpatialZ, Dimensions.SpatialY, Dimensions.SpatialX]
            Note: SpatialY and SpatialX will always be added to the list if not present.

def __init__(
        self,
        data: types.FileLike,
        chunk_by_dims: List[str] = [
            Dimensions.SpatialZ,
            Dimensions.SpatialY,
            Dimensions.SpatialX,
        ],
        S: int = 0,
        **kwargs,
    ):
        # Run super init to check filepath provided
        super().__init__(data, **kwargs)

        # Store parameters needed for _daread
        self.chunk_by_dims = chunk_by_dims
        self.specific_s_index = S

def size_y(self) -> int:
        return self._size_of_dimension(Dimensions.SpatialY)

def _daread(
        img: Path,
        offsets: List[np.ndarray],
        read_lengths: np.ndarray,
        chunk_by_dims: List[str] = [
            Dimensions.SpatialZ,
            Dimensions.SpatialY,
            Dimensions.SpatialX,
        ],
        S: int = 0,
    ) -> Tuple[da.core.Array, str]:
        """
        Read a LIF image file as a delayed dask array where certain dimensions act as
        the chunk size.

        Parameters
        ----------
        img: Path
            The filepath to read.
        offsets: List[numpy.ndarray]
            A List of numpy ndarrays offsets, see _compute_offsets for more details.
        read_lengths: numpy.ndarray
            A 1D numpy array of read lengths, the index is the scene index

def size_y(self) -> int:
        return self._size_of_dimension(Dimensions.SpatialY)

def __init__(
        self,
        data: types.FileLike,
        chunk_by_dims: List[str] = [
            Dimensions.SpatialZ,
            Dimensions.SpatialY,
            Dimensions.SpatialX,
        ],
        S: int = 0,
        **kwargs,
    ):
        # Run super init to check filepath provided
        super().__init__(data, **kwargs)

        # Store parameters needed for _daread
        self.chunk_by_dims = chunk_by_dims
        self.specific_s_index = S
        lif = LifFile(filename=self._file)
        #  _chunk_offsets is a list of ndarrays
        # (only way I could deal with inconsistent scene shape)
        self._chunk_offsets, self._chunk_lengths = LifReader._compute_offsets(lif=lif)

else:
            # If the list is length one that means that all the scenes in the image
            # have the same dimensions
            # Just select the first dictionary in the list
            image_dim_indices = image_dim_indices[0]

        # Uppercase dimensions provided to chunk by dims
        chunk_by_dims = [d.upper() for d in chunk_by_dims]

        # Always add Y and X dims to chunk by dims because that is how CZI files work
        if Dimensions.SpatialY not in chunk_by_dims:
            log.info(
                "Adding the Spatial Y dimension to chunk by dimensions as it was not "
                "found."
            )
            chunk_by_dims.append(Dimensions.SpatialY)
        if Dimensions.SpatialX not in chunk_by_dims:
            log.info(
                "Adding the Spatial X dimension to chunk by dimensions as it was not "
                "found."
            )
            chunk_by_dims.append(Dimensions.SpatialX)

        # Setup read dimensions for an example chunk
        first_chunk_read_dims = {}
        for dim, (dim_begin_index, dim_end_index) in image_dim_indices.items():
            # Only add the dimension if the dimension isn't a part of the chunk
            if dim not in chunk_by_dims:
                # Add to read dims
                first_chunk_read_dims[dim] = dim_begin_index

        # Read first chunk for information used by dask.array.from_delayed

else:
            # If the list is length one that means that all the scenes in the image
            # have the same dimensions
            # Just select the first dictionary in the list
            image_dim_indices = image_dim_indices[0]

        # Uppercase dimensions provided to chunk by dims
        chunk_by_dims = [d.upper() for d in chunk_by_dims]

        # Always add Y and X dims to chunk by dims because that is how LIF files work
        if Dimensions.SpatialY not in chunk_by_dims:
            log.info(
                "Adding the Spatial Y dimension to chunk by dimensions as it was not "
                "found."
            )
            chunk_by_dims.append(Dimensions.SpatialY)
        if Dimensions.SpatialX not in chunk_by_dims:
            log.info(
                "Adding the Spatial X dimension to chunk by dimensions as it was not "
                "found."
            )
            chunk_by_dims.append(Dimensions.SpatialX)

        # Setup read dimensions for an example chunk
        first_chunk_read_dims = {}
        for dim, (dim_begin_index, dim_end_index) in image_dim_indices.items():
            # Only add the dimension if the dimension isn't a part of the chunk
            if dim not in chunk_by_dims:
                # Add to read dims
                first_chunk_read_dims[dim] = dim_begin_index

        # Read first chunk for information used by dask.array.from_delayed

def size_y(self) -> int:
        """
        Returns
        -------
        size: int
            The size of the Spatial Y dimension.
        """
        return self.size(Dimensions.SpatialY)[0]

def size_y(self) -> int:
        """
        Returns
        -------
        size: int
            The size of the Spatial Y dimension.
        """
        return self.size(Dimensions.SpatialY)[0]