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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _sparse_array_op( left: ABCSparseArray, right: ABCSparseArray, op: Callable, name: str ) -> Any: """ Perform a binary operation between two arrays. Parameters left : Union[SparseArray, ndarray] right : Union[SparseArray, ndarray] op : Callable The binary operation to perform name str Name of the callable. Returns ------- SparseArray """

    if name.startswith('__'):
        # For lookups in _libs.sparse we need non-dunder op name
        name = name[2:-2]

    # dtype used to find corresponding sparse method
    ltype = left.dtype.subtype
    rtype = right.dtype.subtype

    if not is_dtype_equal(ltype, rtype):
        subtype = find_common_type([ltype, rtype])
        ltype = SparseDtype(subtype, left.fill_value)
        rtype = SparseDtype(subtype, right.fill_value)

        # TODO(GH-23092): pass copy=False. Need to fix astype_nansafe
        left = left.astype(ltype)
        right = right.astype(rtype)
        dtype = ltype.subtype
    else:
        dtype = ltype

    # dtype the result must have
    result_dtype = None

    if left.sp_index.ngaps == 0 or right.sp_index.ngaps == 0:
        with np.errstate(all='ignore'):
            result = op(left.get_values(), right.get_values())
            fill = op(_get_fill(left), _get_fill(right))

        if left.sp_index.ngaps == 0:
            index = left.sp_index
        else:
            index = right.sp_index
    elif left.sp_index.equals(right.sp_index):
        with np.errstate(all='ignore'):
            result = op(left.sp_values, right.sp_values)
            fill = op(_get_fill(left), _get_fill(right))
        index = left.sp_index
    else:
        if name[0] == 'r':
            left, right = right, left
            name = name[1:]

        if name in ('and', 'or') and dtype == 'bool':
            opname = 'sparse_{name}_uint8'.format(name=name)
            # to make template simple, cast here
            left_sp_values = left.sp_values.view(np.uint8)
            right_sp_values = right.sp_values.view(np.uint8)
            result_dtype = np.bool
        else:
            opname = 'sparse_{name}_{dtype}'.format(name=name, dtype=dtype)
            left_sp_values = left.sp_values
            right_sp_values = right.sp_values

        sparse_op = getattr(splib, opname)

        with np.errstate(all='ignore'):
            result, index, fill = sparse_op(
                left_sp_values, left.sp_index, left.fill_value,
                right_sp_values, right.sp_index, right.fill_value)

    if result_dtype is None:
        result_dtype = result.dtype

    return _wrap_result(name, result, index, fill, dtype=result_dtype)