class_docstr: Constructs a VisionTextDualEncoder processor which wraps an image processor and a tokenizer into a single
  processor.
  
  [`VisionTextDualEncoderProcessor`] offers all the functionalities of [`AutoImageProcessor`] and [`AutoTokenizer`].
  See the [`~VisionTextDualEncoderProcessor.__call__`] and [`~VisionTextDualEncoderProcessor.decode`] for more
  information.
  
  Args:
      image_processor ([`AutoImageProcessor`], *optional*):
          The image processor is a required input.
      tokenizer ([`PreTrainedTokenizer`], *optional*):
          The tokenizer is a required input.
code: def __init__(self, image_processor=None, tokenizer=None, **kwargs):
      feature_extractor = None
      if "feature_extractor" in kwargs:
          warnings.warn(
              "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
              " instead.",
              FutureWarning,
          )
          feature_extractor = kwargs.pop("feature_extractor")
  
      image_processor = (
          image_processor
          if image_processor is not None
          else feature_extractor
      )
      if image_processor is None:
          raise ValueError("You have to specify an image_processor.")
      if tokenizer is None:
          raise ValueError("You have to specify a tokenizer.")
  
      super().__init__(image_processor, tokenizer)
      self.current_processor = self.image_processor
  
file_path: transformers/src/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py
imports: import warnings
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
label_desc: Use image node_id 1 to create a new VisionTextDualEncoderProcessor object from inherited base classes: ProcessorMixin with example: obj = VisionTextDualEncoderProcessor(image_processor, tokenizer)