def ndata(self):
        """Return a node data view for setting/getting node features

        Let ``g`` be a DGLGraph. If ``g`` is a graph of a single node type, ``g.ndata[feat]``
        returns the node feature associated with the name ``feat``. One can also set a node
        feature associated with the name ``feat`` by setting ``g.ndata[feat]`` to a tensor.

        If ``g`` is a graph of multiple node types, ``g.ndata[feat]`` returns a
        dict[str, Tensor] mapping node types to the node features associated with the name
        ``feat`` for the corresponding type. One can also set a node feature associated
        with the name ``feat`` for some node type(s) by setting ``g.ndata[feat]`` to a
        dictionary as described.

        Notes
        -----
        For setting features, the device of the features must be the same as the device
        of the graph.

        Examples
        --------
        The following example uses PyTorch backend.

        >>> import dgl
        >>> import torch

        Set and get feature 'h' for a graph of a single node type.

        >>> g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
        >>> g.ndata['h'] = torch.ones(3, 1)
        >>> g.ndata['h']
        tensor([[1.],
                [1.],
                [1.]])

        Set and get feature 'h' for a graph of multiple node types.

        >>> g = dgl.heterograph({
        ...     ('user', 'follows', 'user'): (torch.tensor([1, 2]), torch.tensor([3, 4])),
        ...     ('player', 'plays', 'game'): (torch.tensor([2, 2]), torch.tensor([1, 1]))
        ... })
        >>> g.ndata['h'] = {'game': torch.zeros(2, 1), 'player': torch.ones(3, 1)}
        >>> g.ndata['h']
        {'game': tensor([[0.], [0.]]),
         'player': tensor([[1.], [1.], [1.]])}
        >>> g.ndata['h'] = {'game': torch.ones(2, 1)}
        >>> g.ndata['h']
        {'game': tensor([[1.], [1.]]),
         'player': tensor([[1.], [1.], [1.]])}

        See Also
        --------
        nodes
        """
        if len(self.ntypes) == 1:
            ntid = self.get_ntype_id(None)
            ntype = self.ntypes[0]
            return HeteroNodeDataView(self, ntype, ntid, ALL)
        else:
            ntids = [self.get_ntype_id(ntype) for ntype in self.ntypes]
            ntypes = self.ntypes
            return HeteroNodeDataView(self, ntypes, ntids, ALL)
Return a node data view for setting/getting node features

        Let ``g`` be a DGLGraph. If ``g`` is a graph of a single node type, ``g.ndata[feat]``
        returns the node feature associated with the name ``feat``. One can also set a node
        feature associated with the name ``feat`` by setting ``g.ndata[feat]`` to a tensor.

        If ``g`` is a graph of multiple node types, ``g.ndata[feat]`` returns a
        dict[str, Tensor] mapping node types to the node features associated with the name
        ``feat`` for the corresponding type. One can also set a node feature associated
        with the name ``feat`` for some node type(s) by setting ``g.ndata[feat]`` to a
        dictionary as described.

        Notes
        -----
        For setting features, the device of the features must be the same as the device
        of the graph.

        Examples
        --------
        The following example uses PyTorch backend.

        >>> import dgl
        >>> import torch

        Set and get feature 'h' for a graph of a single node type.

        >>> g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
        >>> g.ndata['h'] = torch.ones(3, 1)
        >>> g.ndata['h']
        tensor([[1.],
                [1.],
                [1.]])

        Set and get feature 'h' for a graph of multiple node types.

        >>> g = dgl.heterograph({
        ...     ('user', 'follows', 'user'): (torch.tensor([1, 2]), torch.tensor([3, 4])),
        ...     ('player', 'plays', 'game'): (torch.tensor([2, 2]), torch.tensor([1, 1]))
        ... })
        >>> g.ndata['h'] = {'game': torch.zeros(2, 1), 'player': torch.ones(3, 1)}
        >>> g.ndata['h']
        {'game': tensor([[0.], [0.]]),
         'player': tensor([[1.], [1.], [1.]])}
        >>> g.ndata['h'] = {'game': torch.ones(2, 1)}
        >>> g.ndata['h']
        {'game': tensor([[1.], [1.]]),
         'player': tensor([[1.], [1.], [1.]])}

        See Also
        --------
        nodes
        
python/dgl/heterograph.py