class_docstr: Constructs a MGP-STR processor which wraps an image processor and MGP-STR tokenizers into a single
  
  [`MgpstrProcessor`] offers all the functionalities of `ViTImageProcessor`] and [`MgpstrTokenizer`]. See the
  [`~MgpstrProcessor.__call__`] and [`~MgpstrProcessor.batch_decode`] for more information.
  
  Args:
      image_processor (`ViTImageProcessor`, *optional*):
          An instance of `ViTImageProcessor`. The image processor is a required input.
      tokenizer ([`MgpstrTokenizer`], *optional*):
          The tokenizer is a required input.
docstr: Convert a list of lists of bpe token ids into a list of strings by calling bpe tokenizer.
  
  Args:
      sequences (`torch.Tensor`):
          List of tokenized input ids.
  Returns:
      `List[str]`: The list of bpe decoded sentences.
docstr_tok: ["Convert","a","list","of","lists","of","bpe","token","ids","into","a","list","of","strings","by","calling","bpe","tokenizer",".","Args",":","sequences","(","`","torch.Tensor","`",")",":","List","of","tokenized","input","ids",".","Returns",":","`","List","[","str","]","`",":","The","list","of","bpe","decoded","sentences","."]
code: def bpe_decode(self, sequences):
      """
      Convert a list of lists of bpe token ids into a list of strings by calling bpe tokenizer.
  
      Args:
          sequences (`torch.Tensor`):
              List of tokenized input ids.
      Returns:
          `List[str]`: The list of bpe decoded sentences.
      """
      return self.bpe_tokenizer.batch_decode(sequences)
  
file_path: transformers/src/transformers/models/mgp_str/processing_mgp_str.py
imports: import warnings
  from transformers import AutoTokenizer
  from transformers.utils import is_torch_available
  from transformers.utils.generic import ExplicitEnum
  from ...processing_utils import ProcessorMixin
label_desc: Use image node_id 6 for calling the MgpstrProcessor obj's underlying member method code with example usage: obj.bpe_decode(sequences) and returns: self