class_docstr: Constructs an TVP processor which wraps a TVP image processor and a Bert tokenizer into a single processor.
  
  [`TvpProcessor`] offers all the functionalities of [`TvpImageProcessor`] and [`BertTokenizerFast`]. See the
  [`~TvpProcessor.__call__`] and [`~TvpProcessor.decode`] for more information.
  
  Args:
      image_processor ([`TvpImageProcessor`], *optional*):
          The image processor is a required input.
      tokenizer ([`BertTokenizerFast`], *optional*):
          The tokenizer is a required input.
code: def model_input_names(self):
      tokenizer_input_names = self.tokenizer.model_input_names
      image_processor_input_names = (
          self.image_processor.model_input_names
      )
      return list(
          dict.fromkeys(
              tokenizer_input_names + image_processor_input_names
          )
      )
  
file_path: transformers/src/transformers/models/tvp/processing_tvp.py
imports: from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
label_desc: Use image node_id 6 for calling the TvpProcessor obj's underlying member method code with example usage: obj.model_input_names() and returns: list