def assign_textual_node_features(ndata, textset, ntype):
    """
    Assigns numericalized tokens from a torchtext dataset to given block.

    The numericalized tokens would be stored in the block as node features
    with the same name as ``field_name``.

    The length would be stored as another node feature with name
    ``field_name + '__len'``.

    block : DGLGraph
        First element of the compacted blocks, with "dgl.NID" as the
        corresponding node ID in the original graph, hence the index to the
        text dataset.

        The numericalized tokens (and lengths if available) would be stored
        onto the blocks as new node features.
    textset : torchtext.data.Dataset
        A torchtext dataset whose number of examples is the same as that
        of nodes in the original graph.
    """
    node_ids = ndata[dgl.NID].numpy()

    for field_name, field in textset.fields.items():
        examples = [getattr(textset[i], field_name) for i in node_ids]

        tokens, lengths = field.process(examples)

        if not field.batch_first:
            tokens = tokens.t()

        ndata[field_name] = tokens
        ndata[field_name + "__len"] = lengths

    Assigns numericalized tokens from a torchtext dataset to given block.

    The numericalized tokens would be stored in the block as node features
    with the same name as ``field_name``.

    The length would be stored as another node feature with name
    ``field_name + '__len'``.

    block : DGLGraph
        First element of the compacted blocks, with "dgl.NID" as the
        corresponding node ID in the original graph, hence the index to the
        text dataset.

        The numericalized tokens (and lengths if available) would be stored
        onto the blocks as new node features.
    textset : torchtext.data.Dataset
        A torchtext dataset whose number of examples is the same as that
        of nodes in the original graph.
    
benchmarks/benchmarks/model_speed/bench_pinsage.py