How to use the xarray.core.utils.is_dict_like function in xarray

def _coarsen_reshape(self, windows, boundary, side):
        """
        Construct a reshaped-array for coarsen
        """
        if not utils.is_dict_like(boundary):
            boundary = {d: boundary for d in windows.keys()}

        if not utils.is_dict_like(side):
            side = {d: side for d in windows.keys()}

        # remove unrelated dimensions
        boundary = {k: v for k, v in boundary.items() if k in windows}
        side = {k: v for k, v in side.items() if k in windows}

        for d, window in windows.items():
            if window <= 0:
                raise ValueError(f"window must be > 0. Given {window}")

        variable = self
        for d, window in windows.items():
            # trim or pad the object
            size = variable.shape[self._get_axis_num(d)]
            n = int(size / window)
            if boundary[d] == "exact":

def is_alignable(obj):
        return isinstance(obj, (DataArray, Dataset))

    positions = []
    keys = []
    out = []
    targets = []
    no_key = object()
    not_replaced = object()
    for position, variables in enumerate(objects):
        if is_alignable(variables):
            positions.append(position)
            keys.append(no_key)
            targets.append(variables)
            out.append(not_replaced)
        elif is_dict_like(variables):
            current_out = {}
            for k, v in variables.items():
                if is_alignable(v) and k not in indexes:
                    # Skip variables in indexes for alignment, because these
                    # should to be overwritten instead:
                    # https://github.com/pydata/xarray/issues/725
                    # https://github.com/pydata/xarray/issues/3377
                    # TODO(shoyer): doing this here feels super-hacky -- can we
                    # move it explicitly into merge instead?
                    positions.append(position)
                    keys.append(k)
                    targets.append(v)
                    current_out[k] = not_replaced
                else:
                    current_out[k] = v
            out.append(current_out)

def _infer_coords_and_dims(
    shape, coords, dims
) -> "Tuple[Dict[Any, Variable], Tuple[Hashable, ...]]":
    """All the logic for creating a new DataArray"""

    if (
        coords is not None
        and not utils.is_dict_like(coords)
        and len(coords) != len(shape)
    ):
        raise ValueError(
            "coords is not dict-like, but it has %s items, "
            "which does not match the %s dimensions of the "
            "data" % (len(coords), len(shape))
        )

    if isinstance(dims, str):
        dims = (dims,)

    if dims is None:
        dims = ["dim_%s" % n for n in range(len(shape))]
        if coords is not None and len(coords) == len(shape):
            # try to infer dimensions from coords
            if utils.is_dict_like(coords):

def __setitem__(self, key, value):
        """Add an array to this dataset.

        If value is a `DataArray`, call its `select_vars()` method, rename it
        to `key` and merge the contents of the resulting dataset into this
        dataset.

        If value is an `Variable` object (or tuple of form
        ``(dims, data[, attrs])``), add it to this dataset as a new
        variable.
        """
        if utils.is_dict_like(key):
            raise NotImplementedError('cannot yet use a dictionary as a key '
                                      'to set Dataset values')

        self.update({key: value})

def _fix_attributes(attributes):
    attributes = dict(attributes)
    for k in list(attributes):
        if k.lower() == "global" or k.lower().endswith("_global"):
            # move global attributes to the top level, like the netcdf-C
            # DAP client
            attributes.update(attributes.pop(k))
        elif is_dict_like(attributes[k]):
            # Make Hierarchical attributes to a single level with a
            # dot-separated key
            attributes.update(
                {
                    "{}.{}".format(k, k_child): v_child
                    for k_child, v_child in attributes.pop(k).items()
                }
            )
    return attributes

def _item_key_to_tuple(self, key):
        if utils.is_dict_like(key):
            return tuple(key.get(dim, slice(None)) for dim in self.dims)
        else:
            return key

def _check_data_shape(data, coords, dims):
    if data is dtypes.NA:
        data = np.nan
    if coords is not None and utils.is_scalar(data, include_0d=False):
        if utils.is_dict_like(coords):
            if dims is None:
                return data
            else:
                data_shape = tuple(
                    as_variable(coords[k], k).size if k in coords.keys() else 1
                    for k in dims
                )
        else:
            data_shape = tuple(as_variable(coord, "foo").size for coord in coords)
        data = np.full(data_shape, data)
    return data

'for in-place arithmetic operations: %s, %s'
                                 % (list(lhs_data_vars), list(rhs_data_vars)))

            dest_vars = OrderedDict()

            for k in lhs_data_vars:
                if k in rhs_data_vars:
                    dest_vars[k] = f(lhs_vars[k], rhs_vars[k])
                elif join in ["left", "outer"]:
                    dest_vars[k] = f(lhs_vars[k], np.nan)
            for k in rhs_data_vars:
                if k not in dest_vars and join in ["right", "outer"]:
                    dest_vars[k] = f(rhs_vars[k], np.nan)
            return dest_vars

        if utils.is_dict_like(other) and not isinstance(other, Dataset):
            # can't use our shortcut of doing the binary operation with
            # Variable objects, so apply over our data vars instead.
            new_data_vars = apply_over_both(self.data_vars, other,
                                            self.data_vars, other)
            return Dataset(new_data_vars)

        other_coords = getattr(other, 'coords', None)
        ds = self.coords.merge(other_coords)

        if isinstance(other, Dataset):
            new_vars = apply_over_both(self.data_vars, other.data_vars,
                                       self.variables, other.variables)
        else:
            other_variable = getattr(other, 'variable', other)
            new_vars = OrderedDict((k, f(self.variables[k], other_variable))
                                   for k in self.data_vars)

def expand(self, key):
            """Parse key using xarray utils to ensure we have dimension names."""
            if not is_dict_like(key):
                labels = expanded_indexer(key, self.data_array.ndim)
                key = dict(zip(self.data_array.dims, labels))
            return key

def collect_dict_values(
    objects: Iterable[Union[Mapping, Any]], keys: Iterable, fill_value: object = None
) -> List[list]:
    return [
        [obj.get(key, fill_value) if is_dict_like(obj) else obj for obj in objects]
        for key in keys
    ]