class_docstr: Constructs a BridgeTower processor which wraps a Roberta tokenizer and BridgeTower image processor into a single
  processor.
  
  [`BridgeTowerProcessor`] offers all the functionalities of [`BridgeTowerImageProcessor`] and
  [`RobertaTokenizerFast`]. See the docstring of [`~BridgeTowerProcessor.__call__`] and
  [`~BridgeTowerProcessor.decode`] for more information.
  
  Args:
      image_processor (`BridgeTowerImageProcessor`):
          An instance of [`BridgeTowerImageProcessor`]. The image processor is a required input.
      tokenizer (`RobertaTokenizerFast`):
          An instance of ['RobertaTokenizerFast`]. The tokenizer is a required input.
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docstr: This method forwards all its arguments to RobertaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
  refer to the docstring of this method for more information.
docstr_tok: ["This","method","forwards","all","its","arguments","to","RobertaTokenizerFast","'s","[","`","~PreTrainedTokenizer.batch_decode","`","]",".","Please","refer","to","the","docstring","of","this","method","for","more","information","."]
code: def batch_decode(self, *args, **kwargs):
      """
      This method forwards all its arguments to RobertaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
      refer to the docstring of this method for more information.
      """
      return self.tokenizer.batch_decode(*args, **kwargs)
  
file_path: transformers/src/transformers/models/bridgetower/processing_bridgetower.py
imports: from typing import List, Optional, Union
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
  from ...utils import TensorType
label_desc: Use image node_id 3 for calling the BridgeTowerProcessor obj's underlying member method code with example usage: obj.batch_decode() and returns: self