class_docstr: Data collator that will dynamically pad the inputs received, as well as the labels.
  
  Args:
      tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
          The tokenizer used for encoding the data.
      model ([`PreTrainedModel`]):
          The model that is being trained. If set and has the *prepare_decoder_input_ids_from_labels*, use it to
          prepare the *decoder_input_ids*
  
          This is useful when using *label_smoothing* to avoid calculating loss twice.
      padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
          Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
          among:
  
          - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
            is provided).
          - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
            acceptable input length for the model if that argument is not provided.
          - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
            lengths).
      max_length (`int`, *optional*):
          Maximum length of the returned list and optionally padding length (see above).
      pad_to_multiple_of (`int`, *optional*):
          If set will pad the sequence to a multiple of the provided value.
  
          This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
          7.5 (Volta).
      label_pad_token_id (`int`, *optional*, defaults to -100):
          The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).
      return_tensors (`str`):
          The type of Tensor to return. Allowable values are "np", "pt" and "tf".
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code: def __call__(self, features, return_tensors=None):
      if return_tensors is None:
          return_tensors = self.return_tensors
      labels = (
          [feature["labels"] for feature in features]
          if "labels" in features[0].keys()
          else None
      )
      # We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the
      # same length to return tensors.
      if labels is not None:
          max_label_length = max(len(l) for l in labels)
          if self.pad_to_multiple_of is not None:
              max_label_length = (
                  (max_label_length + self.pad_to_multiple_of - 1)
                  // self.pad_to_multiple_of
                  * self.pad_to_multiple_of
              )
  
          padding_side = self.tokenizer.padding_side
          for feature in features:
              remainder = [self.label_pad_token_id] * (
                  max_label_length - len(feature["labels"])
              )
              if isinstance(feature["labels"], list):
                  feature["labels"] = (
                      feature["labels"] + remainder
                      if padding_side == "right"
                      else remainder + feature["labels"]
                  )
              elif padding_side == "right":
                  feature["labels"] = np.concatenate(
                      [feature["labels"], remainder]
                  ).astype(np.int64)
              else:
                  feature["labels"] = np.concatenate(
                      [remainder, feature["labels"]]
                  ).astype(np.int64)
  
      features = self.tokenizer.pad(
          features,
          padding=self.padding,
          max_length=self.max_length,
          pad_to_multiple_of=self.pad_to_multiple_of,
          return_tensors=return_tensors,
      )
  
      # prepare decoder_input_ids
      if (
          labels is not None
          and self.model is not None
          and hasattr(
              self.model, "prepare_decoder_input_ids_from_labels"
          )
      ):
          decoder_input_ids = (
              self.model.prepare_decoder_input_ids_from_labels(
                  labels=features["labels"]
              )
          )
          features["decoder_input_ids"] = decoder_input_ids
  
      return features
  
imports: import random
  import warnings
  from collections.abc import Mapping
  from dataclasses import dataclass
  from random import randint
  from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
  import numpy
  from ..models.bert import BertTokenizer, BertTokenizerFast
  from ..tokenization_utils_base import PreTrainedTokenizerBase
  from ..utils import PaddingStrategy
label_desc: Use image node_id 1 for calling the DataCollatorForSeq2Seq obj's underlying member method code with example usage: obj.__call__(features, return_tensors) and returns: features