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The lasiest way is hacking an existing method: you can just mimic the code of the to_directed and add a loop iterating over subsets of (possibly labeled, possibly multiple,...) edges as follows (note that you can see the code of to_directed by typing G.to_directed?? for some graph G).

The only differences will be:

give the orientation name to the function
a loop over the orientations:
```
for orient in subsets(self.edges()):
```
an if then else test to know whether we have to add one edge or the other
return D is replaced with yield D to make an iterator (instead of returning the first orientation found).

So, the code you might get is:

def orientations(self, implementation='c_graph', data_structure=None,
                sparse=None):
    """
    Iterates over all orientations of self.

    INPUT:

     - ``data_structure`` -- one of ``"sparse"``, ``"static_sparse"``, or
       ``"dense"``. See the documentation of :class:`Graph` or
       :class:`DiGraph`.

     - ``sparse`` (boolean) -- ``sparse=True`` is an alias for
       ``data_structure="sparse"``, and ``sparse=False`` is an alias for
       ``data_structure="dense"``.
    """
    if sparse is not None:
        if data_structure is not None:
            raise ValueError("The 'sparse' argument is an alias for "
                             "'data_structure'. Please do not define both.")
        data_structure = "sparse" if sparse else "dense"
    if data_structure is None:
        from sage.graphs.base.dense_graph import DenseGraphBackend
        from sage.graphs.base.sparse_graph import SparseGraphBackend
        if isinstance(self._backend, DenseGraphBackend):
            data_structure = "dense"
        elif isinstance(self._backend, SparseGraphBackend):
            data_structure = "sparse"
        else:
            data_structure = "static_sparse"
    from sage.graphs.all import DiGraph
    for orient in subsets(self.edges()):
        D = DiGraph(name           = self.name(),
                    pos            = self._pos,
                    multiedges     = self.allows_multiple_edges(),
                    loops          = self.allows_loops(),
                    implementation = implementation,
                    data_structure = (data_structure if data_structure!="static_sparse"
                                      else "sparse")) # we need a mutable copy
        D.add_vertices(self.vertex_iterator())
        for u,v,l in self.edge_iterator():
            if (u,v,l) in orient:
                D.add_edge(u,v,l)
            else:
                D.add_edge(v,u,l)
        if hasattr(self, '_embedding'):
            D._embedding = copy(self._embedding)
        D._weighted = self._weighted
        if data_structure == "static_sparse":
            D = D.copy(data_structure=data_structure)
        yield D

You can test it on a small example to see if it satisfies your need:

sage: G = graphs.CycleGraph(3)
sage: G.plot()
sage: for i in orientations(G):
....:     i.plot()

It should also work for multiedges graphs, you can decide which implementation/data structure you want for the oriented graphs, the sparsity, etc.

The only differences will be:

give the orientation name to the function
a loop over the orientations:
```
for orient in subsets(self.edges()):
```
an if then else test to know whether we have to add one edge or the other
return D is replaced with yield D to make an iterator (instead of returning the first orientation found).

So, the code you might get is:

def orientations(self, implementation='c_graph', data_structure=None,
                sparse=None):
    """
    Iterates over all orientations of self.

    INPUT:

     - ``data_structure`` -- one of ``"sparse"``, ``"static_sparse"``, or
       ``"dense"``. See the documentation of :class:`Graph` or
       :class:`DiGraph`.

     - ``sparse`` (boolean) -- ``sparse=True`` is an alias for
       ``data_structure="sparse"``, and ``sparse=False`` is an alias for
       ``data_structure="dense"``.
    """
    if sparse is not None:
        if data_structure is not None:
            raise ValueError("The 'sparse' argument is an alias for "
                             "'data_structure'. Please do not define both.")
        data_structure = "sparse" if sparse else "dense"
    if data_structure is None:
        from sage.graphs.base.dense_graph import DenseGraphBackend
        from sage.graphs.base.sparse_graph import SparseGraphBackend
        if isinstance(self._backend, DenseGraphBackend):
            data_structure = "dense"
        elif isinstance(self._backend, SparseGraphBackend):
            data_structure = "sparse"
        else:
            data_structure = "static_sparse"
    from sage.graphs.all import DiGraph
    for orient in subsets(self.edges()):
        D = DiGraph(name           = self.name(),
                    pos            = self._pos,
                    multiedges     = self.allows_multiple_edges(),
                    loops          = self.allows_loops(),
                    implementation = implementation,
                    data_structure = (data_structure if data_structure!="static_sparse"
                                      else "sparse")) # we need a mutable copy
        D.add_vertices(self.vertex_iterator())
        for u,v,l in self.edge_iterator():
            if (u,v,l) in orient:
                D.add_edge(u,v,l)
            else:
                D.add_edge(v,u,l)
        if hasattr(self, '_embedding'):
            D._embedding = copy(self._embedding)
        D._weighted = self._weighted
        if data_structure == "static_sparse":
            D = D.copy(data_structure=data_structure)
        yield D

You can test it on a small example to see if it satisfies your need:

sage: G = graphs.CycleGraph(3)
sage: G.plot()
sage: for i in orientations(G):
....:     i.plot()

It should also work for multiedges graphs, you can decide which implementation/data structure you want for the oriented graphs, the sparsity, to keep the embedding, etc.

EDIT see also trac ticket 21415.