How to use the sparse._coo.core.COO function in sparse

-------
        COO
            The converted :obj:`COO` object.

        Examples
        --------
        >>> x = scipy.sparse.rand(6, 3, density=0.2)
        >>> s = COO.from_scipy_sparse(x)
        >>> np.array_equal(x.todense(), s.todense())
        True
        """
        x = x.asformat("coo")
        coords = np.empty((2, x.nnz), dtype=x.row.dtype)
        coords[0, :] = x.row
        coords[1, :] = x.col
        return COO(
            coords,
            x.data,
            shape=x.shape,
            has_duplicates=not x.has_canonical_format,
            sorted=x.has_canonical_format,
        )

"""
    from .core import COO

    check_zero_fill_value(x)

    if not x.ndim >= 2:
        raise NotImplementedError(
            "sparse.triu is not implemented for scalars or 1-D arrays."
        )

    mask = x.coords[-2] + k <= x.coords[-1]

    coords = x.coords[:, mask]
    data = x.data[mask]

    return COO(coords, data, shape=x.shape, has_duplicates=False, sorted=True)

self.data,
                shape=self.shape,
                fill_value=self.fill_value,
            )
        if self.ndim == 1:
            return COO(
                self.indices[None, :],
                self.data,
                shape=self.shape,
                fill_value=self.fill_value,
            )
        uncompressed = uncompress_dimension(self.indptr)
        coords = np.vstack((uncompressed, self.indices))
        order = np.argsort(self._axis_order)
        return (
            COO(
                coords,
                self.data,
                shape=self._compressed_shape,
                fill_value=self.fill_value,
            )
            .reshape(self._reordered_shape)
            .transpose(order)
        )

return None

        func_coords = matched_arrays[0].coords[:, unmatched_mask]
        func_data = func_data[unmatched_mask]

        if matched_arrays[0].shape != self.shape:
            params = _get_broadcast_parameters(matched_arrays[0].shape, self.shape)
            func_coords, func_data = _get_expanded_coords_data(
                func_coords, func_data, params, self.shape
            )

        if all(m is None or m for m in mask):
            return func_coords, func_data

        # Not really sorted but we need the sortedness.
        func_array = COO(
            func_coords, func_data, self.shape, has_duplicates=False, sorted=True
        )

        unmatched_mask = np.ones(func_array.nnz, dtype=np.bool)

        for arg in unmatched_args:
            matched_idx = self._match_coo(func_array, arg, return_midx=True)[0]
            unmatched_mask[matched_idx] = False

        coords = np.asarray(func_array.coords[:, unmatched_mask], order="C")
        data = np.asarray(func_array.data[unmatched_mask], order="C")

        return coords, data

shape = list(arrays[0].shape)
    shape.insert(axis, len(arrays))

    nnz = 0
    dim = 0
    new = np.empty(shape=(coords.shape[1],), dtype=np.intp)
    for x in arrays:
        new[nnz : x.nnz + nnz] = dim
        dim += 1
        nnz += x.nnz

    coords = [coords[i] for i in range(coords.shape[0])]
    coords.insert(axis, new)
    coords = np.stack(coords, axis=0)

    return COO(
        coords,
        data,
        shape=shape,
        has_duplicates=False,
        sorted=(axis == 0),
        fill_value=arrays[0].fill_value,
    )

if isinstance(index, str):
        data = x.data[index]
        idx = np.where(data)
        data = data[idx].flatten()
        coords = list(x.coords[:, idx[0]])
        coords.extend(idx[1:])

        fill_value_idx = np.asarray(x.fill_value[index]).flatten()
        fill_value = (
            fill_value_idx[0] if fill_value_idx.size else _zero_of_dtype(data.dtype)[()]
        )

        if not equivalent(fill_value, fill_value_idx).all():
            raise ValueError("Fill-values in the array are inconsistent.")

        return COO(
            coords,
            data,
            shape=x.shape + x.data.dtype[index].shape,
            has_duplicates=False,
            sorted=True,
            fill_value=fill_value,
        )

    # Otherwise, convert into a tuple.
    if not isinstance(index, tuple):
        index = (index,)

    # Check if the last index is an ellipsis.
    last_ellipsis = len(index) > 0 and index[-1] is Ellipsis

    # Normalize the index into canonical form.

def tocoo(self):
        """
        Convert this :obj:`GCXS` array to a :obj:`COO`. 

        Returns
        -------
        sparse.COO
            The converted COO array.
        """
        if self.ndim == 0:
            return COO(
                np.array([])[None],
                self.data,
                shape=self.shape,
                fill_value=self.fill_value,
            )
        if self.ndim == 1:
            return COO(
                self.indices[None, :],
                self.data,
                shape=self.shape,
                fill_value=self.fill_value,
            )
        uncompressed = uncompress_dimension(self.indptr)
        coords = np.vstack((uncompressed, self.indices))
        order = np.argsort(self._axis_order)
        return (

if method == "outer":
            method = "__call__"

            cum_ndim = 0
            inputs_transformed = []
            for inp in inputs:
                inputs_transformed.append(inp[(Ellipsis,) + (None,) * cum_ndim])
                cum_ndim += inp.ndim

            inputs = tuple(inputs_transformed)

        if method == "__call__":
            result = elemwise(ufunc, *inputs, **kwargs)
        elif method == "reduce":
            result = COO._reduce(ufunc, *inputs, **kwargs)
        else:
            return NotImplemented

        if out is not None:
            (out,) = out
            if out.shape != result.shape:
                raise ValueError(
                    "non-broadcastable output operand with shape %s "
                    "doesn't match the broadcast shape %s" % (out.shape, result.shape)
                )

            out._make_shallow_copy_of(result)
            return out

        return result

elif not isinstance(x[0][0], Iterable):
            coords = np.stack(x[1], axis=0)
            data = np.asarray(x[0], dtype=dtype)
        else:
            coords = np.array([item[0] for item in x]).T
            data = np.array([item[1] for item in x], dtype=dtype)

        if not (
            coords.ndim == 2
            and data.ndim == 1
            and np.issubdtype(coords.dtype, np.integer)
            and np.all(coords >= 0)
        ):
            raise ValueError("Invalid iterable to convert to COO.")

        return COO(coords, data, shape=shape, fill_value=fill_value)