class_docstr: Constructs a MarkupLM processor which combines a MarkupLM feature extractor and a MarkupLM tokenizer into a single
  processor.
  
  [`MarkupLMProcessor`] offers all the functionalities you need to prepare data for the model.
  
  It first uses [`MarkupLMFeatureExtractor`] to extract nodes and corresponding xpaths from one or more HTML strings.
  Next, these are provided to [`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`], which turns them into token-level
  `input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and `xpath_subs_seq`.
  
  Args:
      feature_extractor (`MarkupLMFeatureExtractor`):
          An instance of [`MarkupLMFeatureExtractor`]. The feature extractor is a required input.
      tokenizer (`MarkupLMTokenizer` or `MarkupLMTokenizerFast`):
          An instance of [`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`]. The tokenizer is a required input.
      parse_html (`bool`, *optional*, defaults to `True`):
          Whether or not to use `MarkupLMFeatureExtractor` to parse HTML strings into nodes and corresponding xpaths.
docstr: This method forwards all its arguments to TrOCRTokenizer'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 TrOCRTokenizer'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/markuplm/processing_markuplm.py
imports: from typing import Optional, Union
  from ...file_utils import TensorType
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
label_desc: Use image node_id 2 for calling the MarkupLMProcessor obj's underlying member method code with example usage: obj.batch_decode() and returns: self