def drop_isel(self, indexers=None, **indexers_kwargs) -> Self:
        """Drop index positions from this Dataset.

        Parameters
        ----------
        indexers : mapping of hashable to Any
            Index locations to drop
        **indexers_kwargs : {dim: position, ...}, optional
            The keyword arguments form of ``dim`` and ``positions``

        Returns
        -------
        dropped : Dataset

        Raises
        ------
        IndexError

        Examples
        --------
        >>> data = np.arange(6).reshape(2, 3)
        >>> labels = ["a", "b", "c"]
        >>> ds = xr.Dataset({"A": (["x", "y"], data), "y": labels})
        >>> ds
        <xarray.Dataset> Size: 60B
        Dimensions:  (x: 2, y: 3)
        Coordinates:
          * y        (y) <U1 12B 'a' 'b' 'c'
        Dimensions without coordinates: x
        Data variables:
            A        (x, y) int64 48B 0 1 2 3 4 5
        >>> ds.drop_isel(y=[0, 2])
        <xarray.Dataset> Size: 20B
        Dimensions:  (x: 2, y: 1)
        Coordinates:
          * y        (y) <U1 4B 'b'
        Dimensions without coordinates: x
        Data variables:
            A        (x, y) int64 16B 1 4
        >>> ds.drop_isel(y=1)
        <xarray.Dataset> Size: 40B
        Dimensions:  (x: 2, y: 2)
        Coordinates:
          * y        (y) <U1 8B 'a' 'c'
        Dimensions without coordinates: x
        Data variables:
            A        (x, y) int64 32B 0 2 3 5
        """

        indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "drop_isel")

        ds = self
        dimension_index = {}
        for dim, pos_for_dim in indexers.items():
            # Don't cast to set, as it would harm performance when labels
            # is a large numpy array
            if utils.is_scalar(pos_for_dim):
                pos_for_dim = [pos_for_dim]
            pos_for_dim = np.asarray(pos_for_dim)
            index = self.get_index(dim)
            new_index = index.delete(pos_for_dim)
            dimension_index[dim] = new_index
        ds = ds.loc[dimension_index]
        return ds
Drop index positions from this Dataset.

        Parameters
        ----------
        indexers : mapping of hashable to Any
            Index locations to drop
        **indexers_kwargs : {dim: position, ...}, optional
            The keyword arguments form of ``dim`` and ``positions``

        Returns
        -------
        dropped : Dataset

        Raises
        ------
        IndexError

        Examples
        --------
        >>> data = np.arange(6).reshape(2, 3)
        >>> labels = ["a", "b", "c"]
        >>> ds = xr.Dataset({"A": (["x", "y"], data), "y": labels})
        >>> ds
        <xarray.Dataset> Size: 60B
        Dimensions:  (x: 2, y: 3)
        Coordinates:
          * y        (y) <U1 12B 'a' 'b' 'c'
        Dimensions without coordinates: x
        Data variables:
            A        (x, y) int64 48B 0 1 2 3 4 5
        >>> ds.drop_isel(y=[0, 2])
        <xarray.Dataset> Size: 20B
        Dimensions:  (x: 2, y: 1)
        Coordinates:
          * y        (y) <U1 4B 'b'
        Dimensions without coordinates: x
        Data variables:
            A        (x, y) int64 16B 1 4
        >>> ds.drop_isel(y=1)
        <xarray.Dataset> Size: 40B
        Dimensions:  (x: 2, y: 2)
        Coordinates:
          * y        (y) <U1 8B 'a' 'c'
        Dimensions without coordinates: x
        Data variables:
            A        (x, y) int64 32B 0 2 3 5
        
xarray/core/dataset.py