private IndexedTensor createTensorRepresentation(List<Integer> input, String dimension)  {
        int size = input.size();
        TensorType type = new TensorType.Builder(TensorType.Value.FLOAT).indexed(dimension, size).build();
        IndexedTensor.Builder builder = IndexedTensor.Builder.of(type);
        for (int i = 0; i < size; ++i) {
            builder.cell(input.get(i), i);
        }
        return builder.build();
    }
A BERT Base compatible embedder. This embedder uses a WordPiece embedder to
produce a token sequence that is then input to a transformer model. A BERT base
compatible transformer model must have three inputs:

 - A token sequence (input_ids)
 - An attention mask (attention_mask)
 - Token types for cross encoding (token_type_ids)

See bert-base-embedder.def for configurable parameters.

@author lesters
model-integration/src/main/java/ai/vespa/embedding/BertBaseEmbedder.java