class_docstr: Constructs a VisionTextDualEncoder processor which wraps a vision feature extractor and a tokenizer into a single
  processor.
  
  [`VisionTextDualEncoderProcessor`] offers all the functionalities of [`AutoFeatureExtractor`] and
  [`AutoTokenizer`]. See the [`~VisionTextDualEncoderProcessor.__call__`] and
  [`~VisionTextDualEncoderProcessor.decode`] for more information.
  
  Args:
      feature_extractor ([`AutoFeatureExtractor`]):
          The feature extractor is a required input.
      tokenizer ([`PreTrainedTokenizer`]):
          The tokenizer is a required input.
docstr: Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
  and `kwargs` arguments to VisionTextDualEncoderTokenizer's [`~PreTrainedTokenizer.__call__`] if `text` is not
  `None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
  AutoFeatureExtractor's [`~AutoFeatureExtractor.__call__`] if `images` is not `None`. Please refer to the
  doctsring of the above two methods for more information.
  
  Args:
      text (`str`, `List[str]`, `List[List[str]]`):
          The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
          (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
          `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
      images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
          The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
          tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
          number of channels, H and W are image height and width.
  
      return_tensors (`str` or [`~utils.TensorType`], *optional*):
          If set, will return tensors of a particular framework. Acceptable values are:
  
          - `'tf'`: Return TensorFlow `tf.constant` objects.
          - `'pt'`: Return PyTorch `torch.Tensor` objects.
          - `'np'`: Return NumPy `np.ndarray` objects.
          - `'jax'`: Return JAX `jnp.ndarray` objects.
  
  Returns:
      [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
  
      - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
      - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
        `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
        `None`).
      - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
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code: def __call__(
      self, text=None, images=None, return_tensors=None, **kwargs
  ):
      """
      Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
      and `kwargs` arguments to VisionTextDualEncoderTokenizer's [`~PreTrainedTokenizer.__call__`] if `text` is not
      `None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
      AutoFeatureExtractor's [`~AutoFeatureExtractor.__call__`] if `images` is not `None`. Please refer to the
      doctsring of the above two methods for more information.
  
      Args:
          text (`str`, `List[str]`, `List[List[str]]`):
              The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
              (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
              `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
          images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
              The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
              tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
              number of channels, H and W are image height and width.
  
          return_tensors (`str` or [`~utils.TensorType`], *optional*):
              If set, will return tensors of a particular framework. Acceptable values are:
  
              - `'tf'`: Return TensorFlow `tf.constant` objects.
              - `'pt'`: Return PyTorch `torch.Tensor` objects.
              - `'np'`: Return NumPy `np.ndarray` objects.
              - `'jax'`: Return JAX `jnp.ndarray` objects.
  
      Returns:
          [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
  
          - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
          - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
            `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
            `None`).
          - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
      """
  
      if text is None and images is None:
          raise ValueError(
              "You have to specify either text or images. Both cannot be none."
          )
  
      if text is not None:
          encoding = self.tokenizer(
              text, return_tensors=return_tensors, **kwargs
          )
  
      if images is not None:
          image_features = self.feature_extractor(
              images, return_tensors=return_tensors, **kwargs
          )
  
      if text is not None and images is not None:
          encoding["pixel_values"] = image_features.pixel_values
          return encoding
      elif text is not None:
          return encoding
      else:
          return BatchEncoding(
              data=dict(**image_features), tensor_type=return_tensors
          )
  
imports: from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
label_desc: Use image node_id 2 for calling the VisionTextDualEncoderProcessor obj's underlying member method code with example usage: obj.__call__(text, images, return_tensors) and returns: encoding, encoding, BatchEncoding