How to use the param.List function in param

Whether the object should be aligned with the start, end or
        center of its container""")

    aspect_ratio = param.Parameter(default=None, doc="""
        Describes the proportional relationship between component's
        width and height.  This works if any of component's dimensions
        are flexible in size. If set to a number, ``width / height =
        aspect_ratio`` relationship will be maintained.  Otherwise, if
        set to ``"auto"``, component's preferred width and height will
        be used to determine the aspect (if not set, no aspect will be
        preserved).""")

    background = param.Parameter(default=None, doc="""
        Background color of the component.""")

    css_classes = param.List(default=None, doc="""
        CSS classes to apply to the layout.""")

    width = param.Integer(default=None, bounds=(0, None), doc="""
        The width of the component (in pixels). This can be either
        fixed or preferred width, depending on width sizing policy.""")

    height = param.Integer(default=None, bounds=(0, None), doc="""
        The height of the component (in pixels).  This can be either
        fixed or preferred height, depending on height sizing policy.""")

    min_width = param.Integer(default=None, bounds=(0, None), doc="""
        Minimal width of the component (in pixels) if width is adjustable.""")

    min_height = param.Integer(default=None, bounds=(0, None), doc="""
        Minimal height of the component (in pixels) if height is adjustable.""")

"""
        Should return the bokeh model to be rendered. 
        """
        return process_plot(self.object, doc, plot_id, comm)


class WidgetBox(Plot):
    """
    A wrapper for bokeh WidgetBox and parambokeh.Widgets making them
    displayable in the notebook.
    """


class Layout(Viewable):

    children = param.List(default=[])

    _bokeh_model = None

    __abstract = True

    def __init__(self, *children, **params):
        super(Layout, self).__init__(children=list(children), **params)
    
    def _get_model(self, doc, comm=None, plot_id=None):
        """
        Should return the bokeh model to be rendered. 
        """
        model = self._bokeh_model()
        plot_id = model.ref['id'] if plot_id is None else plot_id
        children = []
        for child in self.children:

class Raster(Element2D):
    """
    Raster is a basic 2D element type for presenting either numpy or
    dask arrays as two dimensional raster images.

    Arrays with a shape of (N,M) are valid inputs for Raster whereas
    subclasses of Raster (e.g. RGB) may also accept 3D arrays
    containing channel information.

    Raster does not support slicing like the Image or RGB subclasses
    and the extents are in matrix coordinates if not explicitly
    specified.
    """

    kdims = param.List(default=[Dimension('x'), Dimension('y')],
                       bounds=(2, 2), constant=True, doc="""
        The label of the x- and y-dimension of the Raster in form
        of a string or dimension object.""")

    group = param.String(default='Raster', constant=True)

    vdims = param.List(default=[Dimension('z')],
                       bounds=(1, 1), doc="""
        The dimension description of the data held in the matrix.""")

    def __init__(self, data, kdims=None, vdims=None, extents=None, **params):
        if extents is None:
            (d1, d2) = data.shape[:2]
            extents = (0, 0, d2, d1)
        super(Raster, self).__init__(data, kdims=kdims, vdims=vdims, extents=extents, **params)

from . import util



class Dataset(Element):
    """
    Dataset provides a general baseclass for Element types that
    contain structured data and supports a range of data formats.

    The Dataset class supports various methods offering a consistent way
    of working with the stored data regardless of the storage format
    used. These operations include indexing, selection and various ways
    of aggregating or collapsing the data with a supplied function.
    """

    datatype = param.List(['array', 'dataframe', 'dictionary', 'grid', 'ndelement'],
        doc=""" A priority list of the data types to be used for storage
        on the .data attribute. If the input supplied to the element
        constructor cannot be put into the requested format, the next
        format listed will be used until a suitable format is found (or
        the data fails to be understood).""")

    # In the 1D case the interfaces should not automatically add x-values
    # to supplied data
    _1d = False

    def __init__(self, data, **kwargs):
        if isinstance(data, Element):
            pvals = util.get_param_values(data)
            kwargs.update([(l, pvals[l]) for l in ['group', 'label']
                           if l in pvals and l not in kwargs])
        initialized = Interface.initialize(type(self), data,

of HoloViews components with whatever level of nesting
    by inserting the Collators key dimensions on the HoloMaps.
    If the items in the Collator do not contain HoloMaps
    they will be created. Collator also supports filtering
    of Tree structures and dropping of constant dimensions.
    """

    drop = param.List(default=[], doc="""
        List of dimensions to drop when collating data, specified
        as strings.""")

    drop_constant = param.Boolean(default=False, doc="""
        Whether to demote any non-varying key dimensions to
        constant dimensions.""")

    filters = param.List(default=[], doc="""
        List of paths to drop when collating data, specified
        as strings or tuples.""")

    group = param.String(default='Collator')


    progress_bar = param.Parameter(default=None, doc="""
         The progress bar instance used to report progress. Set to
         None to disable progress bars.""")

    merge_type = param.ClassSelector(class_=NdMapping, default=HoloMap,
                                     is_instance=False,instantiate=False)

    value_transform = param.Callable(default=None, doc="""
        If supplied the function will be applied on each Collator
        value during collation. This may be used to apply an operation

return ""



class DynamicMap(HoloMap):
    """
    A DynamicMap is a type of HoloMap where the elements are dynamically
    generated by a callable. The callable is invoked with values
    associated with the key dimensions or with values supplied by stream
    parameters.
    """

    # Declare that callback is a positional parameter (used in clone)
    __pos_params = ['callback']

    kdims = param.List(default=[], constant=True, doc="""
        The key dimensions of a DynamicMap map to the arguments of the
        callback. This mapping can be by position or by name.""")

    callback = param.ClassSelector(class_=Callable, constant=True, doc="""
        The callable used to generate the elements. The arguments to the
        callable includes any number of declared key dimensions as well
        as any number of stream parameters defined on the input streams.

        If the callable is an instance of Callable it will be used
        directly, otherwise it will be automatically wrapped in one.""")

    streams = param.List(default=[], constant=True, doc="""
       List of Stream instances to associate with the DynamicMap. The
       set of parameter values across these streams will be supplied as
       keyword arguments to the callback when the events are received,
       updating the streams.""" )

kernel pattern.

    The bounds and densities of the supplied kernel pattern do not
    affect the convolution operation. The spatial scale of the
    convolution is determined by the 'size' parameter of the
    kernel. The resulting convolution is applied of a spatial scale
    relative to the overall size of the input, as expressed in
    sheetcoordinates.
    """

    kernel_pattern = param.ClassSelector(PatternGenerator,
                     default=Gaussian(size=0.05,aspect_ratio=1.0), doc="""
      The kernel pattern used in the convolution. The default kernel
      results in an isotropic Gaussian blur.""")

    init_keys = param.List(default=['SCS'], constant=True)

    def __init__(self, **params):
        super(Convolve,self).__init__(**params)


    def initialize(self,  **kwargs):
        super(Convolve, self).initialize(**kwargs)
        scs = kwargs['SCS']
        pattern_copy = copy.deepcopy(self.kernel_pattern)
        pattern_copy.set_matrix_dimensions(self.kernel_pattern.bounds,
                                           scs.xdensity,
                                           scs.ydensity)
        self.kernel = pattern_copy()

    def __call__(self, x):
        if not hasattr(self, 'kernel'):

if isinstance(v, param.Parameter):
                params[k] = v
            elif isinstance(v, bool):
                params[k] = param.Boolean(**kws)
            elif isinstance(v, int):
                params[k] = param.Integer(**kws)
            elif isinstance(v, float):
                params[k] = param.Number(**kws)
            elif isinstance(v,str):
                params[k] = param.String(**kws)
            elif isinstance(v,dict):
                params[k] = param.Dict(**kws)
            elif isinstance(v, tuple):
                params[k] = param.Tuple(**kws)
            elif isinstance(v,list):
                params[k] = param.List(**kws)
            elif isinstance(v,np.ndarray):
                params[k] = param.Array(**kws)
            else:
                params[k] = param.Parameter(**kws)

        # Dynamic class creation using type
        return type(name, (Stream,), params)

params['point_columns'] = dimensions
        super(PointTableLink, self).__init__(source, target, **params)


class PointTableSelectionLink(Link):

    _requires_target = True


class VertexTableLink(Link):
    """
    Defines a Link between a Path type and a Table which will
    display the vertices of selected path.
    """

    vertex_columns = param.List(default=[])

    _requires_target = True

    def __init__(self, source, target, **params):
        if 'vertex_columns' not in params:
            dimensions = [dimension_sanitizer(d.name) for d in target.dimensions()[:2]]
            params['vertex_columns'] = dimensions
        super(VertexTableLink, self).__init__(source, target, **params)


class PointTableSelectionLinkCallback(LinkCallback):

    source_model = 'selected'
    target_model = 'selected'

    on_source_changes = ['indices']

class Annotation(Element2D):
    """
    An Annotation is a special type of element that is designed to be
    overlaid on top of any arbitrary 2D element. Annotations have
    neither key nor value dimensions allowing them to be overlaid over
    any type of data.

    Note that one or more Annotations *can* be displayed without being
    overlaid on top of any other data. In such instances (by default)
    they will be displayed using the unit axis limits (0.0-1.0 in both
    directions) unless an explicit 'extents' parameter is
    supplied. The extents of the bottom Annotation in the Overlay is
    used when multiple Annotations are displayed together.
    """

    kdims = param.List(default=[Dimension('x'), Dimension('y')],
                       bounds=(2,2))

    group = param.String(default='Annotation', constant=True)

    _auxiliary_component = True

    def __init__(self, data, **params):
        super(Annotation, self).__init__(data, **params)

    def __len__(self):
        return 1

    def __getitem__(self, key):
        if key in self.dimensions(): return self.dimension_values(key)
        if not isinstance(key, tuple) or len(key) == 1:
            key = (key, slice(None))