class_docstr: Transformer tokens and outputs for one Doc object.
  
  The transformer models return tensors that refer to a whole padded batch
  of documents. These tensors are wrapped into the FullTransformerBatch object.
  The FullTransformerBatch then splits out the per-document data, which is
  handled by this class. Instances of this class are typically assigned to
  the doc._.trf_data extension attribute.
  
  Attributes
  ----------
  wordpieces (WordpieceBatch): A slice of the wordpiece token data produced
      by the Huggingface tokenizer.
  model_output (ModelOutput): The model output from the transformer model,
      determined by the model and transformer config.
  align (Ragged): Alignment from the Doc's tokenization to the wordpieces.
      This is a ragged array, where align.lengths[i] indicates the number of
      wordpiece tokens that token i aligns against. The actual indices are
      provided at align[i].dataXd.
code: def tensors(self) -> Tuple[Union[FloatsXd, List[FloatsXd]]]:
      return self.model_output.to_tuple()
  
imports: from typing import Optional, List, Dict, Any, Union, Tuple, cast
  from dataclasses import dataclass, field
  import torch
  import numpy
  from transformers.tokenization_utils import BatchEncoding
  from transformers.file_utils import ModelOutput
  from transformers.modeling_outputs import BaseModelOutput
  from thinc.types import Ragged, Floats2d, Floats3d, FloatsXd, Ints1d, Ints2d
  from thinc.api import NumpyOps, get_array_module, xp2torch, torch2xp
  from spacy.tokens import Span
  import srsly
  from .util import transpose_list
  from .align import get_token_positions
label_desc: Use image node_id 3 for calling the TransformerData obj's underlying member method code with example usage: obj.tensors() and returns: self